nils-herrmann/clean_scirepeval huggingface dataset:
Bit-Cell Level Optimization for Non-volatile Memories Using Magnetic Tunnel Junctions and Spin-Transfer Torque Switching

Spin-transfer torque magnetic random access memories (STT-MRAM), using magnetic tunnel junctions (MTJ), is a resistive memory technology that has spurred significant research interest due to its potential for on-chip, high-density, high-speed, low-power, and non-volatile memory. However, due to conflicting read and write requirements, there is a need to develop optimization techniques for designing STT-MRAM bit-cells to minimize read and write failures. We propose an optimization technique that minimizes read and write failures by proper selection of bit-cell configuration and by proper access transistor sizing. A mixed-mode simulation framework was developed to evaluate the effectiveness of our optimization technique. Our simulation framework captures the transport physics in the MTJ using Non-Equilibrium Green's Function method and self-consistently solves the MTJ magnetization dynamics using Landau-Lifshitz-Gilbert equation augmented with the full Slonczewski spin-torque term. The electrical parameters of the MTJ are then encapsulated in a Verilog-A model and used in HSPICE to perform bit-cell level optimization. The optimization technique is applied to STT-MRAM bit-cells designed using 45 nm bulk and 45 nm silicon-on-insulator CMOS technologies. Finally, predictions are made for optimized STT-MRAM bit-cells designed in 16 nm predictive technology.
Textural Features Corresponding to Visual Perception

Textural features corresponding to human visual perception are very useful for optimum feature selection and texture analyzer design. We approximated in computational form six basic textural features, namely, coarseness, contrast, directionality, line-likeness, regularity, and roughness. In comparison with psychological measurements for human subjects, the computational measures gave good correspondences in rank correlation of 16 typical texture patterns. Similarity measurements using these features were attempted. The discrepancies between human vision and computerized techniques that we encountered in this study indicate fundamental problems in digital analysis of textures. Some of them could be overcome by analyzing their causes and using more sophisticated techniques.
Tracking trends: incorporating term volume into temporal topic models

Text corpora with documents from a range of time epochs are natural and ubiquitous in many fields, such as research papers, newspaper articles and a variety of types of recently emerged social media. People not only would like to know what kind of topics can be found from these data sources but also wish to understand the temporal dynamics of these topics and predict certain properties of terms or documents in the future. Topic models are usually utilized to find latent topics from text collections, and recently have been applied to temporal text corpora. However, most proposed models are general purpose models to which no real tasks are explicitly associated. Therefore, current models may be difficult to apply in real-world applications, such as the problems of tracking trends and predicting popularity of keywords. In this paper, we introduce a real-world task, tracking trends of terms, to which temporal topic models can be applied. Rather than building a general-purpose model, we propose a new type of topic model that incorporates the volume of terms into the temporal dynamics of topics and optimizes estimates of term volumes. In existing models, trends are either latent variables or not considered at all which limits the potential for practical use of trend information. In contrast, we combine state-space models with term volumes with a supervised learning model, enabling us to effectively predict the volume in the future, even without new documents. In addition, it is straightforward to obtain the volume of latent topics as a by-product of our model, demonstrating the superiority of utilizing temporal topic models over traditional time-series tools (e.g., autoregressive models) to tackle this kind of problem. The proposed model can be further extended with arbitrary word-level features which are evolving over time. We present the results of applying the model to two datasets with long time periods and show its effectiveness over non-trivial baselines.
Mental Accounting Matters

Mental accounting is the set of cognitive operations used by individuals and households to organize, evaluate, and keep track of financial activities. Making use of research on this topic over the past decade, this paper summarizes the current state of our knowledge about how people engage in mental accounting activities. Three components of mental accounting receive the most attention. This first captures how outcomes are perceived and experienced, and how decisions are made and subsequently evaluated. The accounting system provides the inputs to be both ex ante and ex po.st cost-benefit analyses. A second component of mental accounting involves the assignment of activities to specific accounts. Both the sources and uses of funds are labeled in real as well as in mental accounting systems. Expenditures are grouped into categories (housing, food, etc.) and spending is sometimes constrained by implicit or explicit budgets. The third component of mental accounting concerns the frequency with which accounts are evaluated and 'choice bracketing'. Accounts can be balanced daily, weekly, yearly, and so on, and can be defined narrowly or broadly. Each of the components of mental accounting violates the economic principle of fungibility. As a result, mental accounting influences choice, that is, it matters.
Endless Flyer: A Continuous Flying Drone with Automatic Battery Replacement

Surveillance and monitoring are indispensable in some large areas for purposes such as home security, road patrols, livestock monitoring, wildfire mapping, and ubiquitous sensing. Computer-controlled micro unmanned aerial vehicles (UAVs) have the potential to perform such missions because they can move autonomously in a surveillance area without being constrained by ground obstacles. However, the duration of flights is a serious problem with UAVs. A typical UAV can fly only for about 10 min using currently available Li-Po batteries, which makes it difficult to conduct tasks like aerial surveillance that clearly require longer flying periods. In this study, we developed an automatic battery replacement mechanism that allows UAVs to fly continuously without manual battery replacement along with the suggestion of the scalable and robust usage for the system. We conducted an initial experiment using this system and successfully assessed the possibility of continuous surveillance in both indoor and outdoor environments.
Wearable soft sensing suit for human gait measurement

Wearable robots based on soft materials will augment mobility and performance of the host without restricting natural kinematics. These wearable robots will need wearable soft sensors to monitor the movement of the wearer and robot outside the lab. Until now wearable soft sensors have not demonstrated significant mechanical robustness nor been systematically characterized for human motion studies of walking and running. Here, we present the design and systematic characterization of a soft sensing suit for monitoring hip, knee, and ankle sagittal plane joint angles. We used hyper-elastic strain sensors based on microchannels of liquid metal embedded within elastomer, but refined their design with the use of discretized stiffness gradients to improve mechanical durability. Characterization of individual sensors shows they are compliant (stretch up to 396% at failure and provide joint torque resistances < 0.17%), sensitive (gauge factors > 2.2), and stable (electromechanical specifications changed < 2% over 1500 cycles). We also evaluated the accuracy and variability of the soft sensing suit by comparing it to joint angle data obtained through optical motion capture. The sensing suit had root mean square (RMS) errors of less than 5 degrees for walking speeds and reached a maximum RMS error of 15 degrees for running speeds. The variability of the sensing suit was equivalent to that of the optical motion capture at all speeds. We anticipate that wearable soft sensing will also have applications beyond wearable robotics, such as in medical diagnostics and in human-computer interaction.
Learning to associate: HybridBoosted multi-target tracker for crowded scene

We propose a learning-based hierarchical approach of multi-target tracking from a single camera by progressively associating detection responses into longer and longer track fragments (tracklets) and finally the desired target trajectories. To define tracklet affinity for association, most previous work relies on heuristically selected parametric models; while our approach is able to automatically select among various features and corresponding non-parametric models, and combine them to maximize the discriminative power on training data by virtue of a HybridBoost algorithm. A hybrid loss function is used in this algorithm because the association of tracklet is formulated as a joint problem of ranking and classification: the ranking part aims to rank correct tracklet associations higher than other alternatives; the classification part is responsible to reject wrong associations when no further association should be done. Experiments are carried out by tracking pedestrians in challenging datasets. We compare our approach with state-of-the-art algorithms to show its improvement in terms of tracking accuracy.
Bi-Directional Neural Machine Translation with Synthetic Parallel Data

Despite impressive progress in highresource settings, Neural Machine Translation (NMT) still struggles in lowresource and out-of-domain scenarios, often failing to match the quality of phrasebased translation. We propose a novel technique that combines back-translation and multilingual NMT to improve performance in these difficult cases. Our technique trains a single model for both directions of a language pair, allowing us to back-translate source or target monolingual data without requiring an auxiliary model. We then continue training on the augmented parallel data, enabling a cycle of improvement for a single model that can incorporate any source, target, or parallel data to improve both translation directions. As a byproduct, these models can reduce training and deployment costs significantly compared to uni-directional models. Extensive experiments show that our technique outperforms standard backtranslation in low-resource scenarios, improves quality on cross-domain tasks, and effectively reduces costs across the board.
A comparative study of texture measures with classification based on featured distributions

-This paper evaluates the performance both of some texture measures which have been successfully used in various applications and of some new promising approaches proposed recently. For classification a method based on Kullback discrimination of sample and prototype distributions is used. The classification results for single features with one-dimensional feature value distributions and for pairs of complementary features with two-dimensional distributions are presented Texture analysis Classification Feature distribution Kullback discriminant Performance evaluation Brodatz textures l. I N T R O D U C T I O N Texture is an important characteristic for the analysis of many types of images. A wide variety of measures for discriminating textures have been proposed, c1'2~ Comparative studies to evaluate the performance of some texture measures have been carried out by Weszka et al., ~3~ Du Bufet al. ~4~ and Ohanian and Dubes, ~s) for
Robust Instance Recognition in Presence of Occlusion and Clutter

We present a robust learning based instance recognition framework from single view point clouds. Our framework is able to handle real-world instance recognition challenges, i.e, clutter, similar looking distractors and occlusion. Recent algorithms have separately tried to address the problem of clutter [9] and occlusion [16] but fail when these challenges are combined. In comparison we handle all challenges within a single framework. Our framework uses a soft label Random Forest  to learn discriminative shape features of an object and use them to classify both its location and pose. We propose a novel iterative training scheme for forests which maximizes the margin between classes to improve recognition accuracy, as compared to a conventional training procedure. The learnt forest outperforms template matching, DPM [7] in presence of similar looking distractors. Using occlusion information, computed from the depth data, the forest learns to emphasize the shape features from the visible regions thus making it robust to occlusion. We benchmark our system with the state-of-the-art recognition systems [9, 7] in challenging scenes drawn from the largest publicly available dataset. To complement the lack of occlusion tests in this dataset, we introduce our Desk3D dataset and demonstrate that our algorithm outperforms other methods in all settings.
Learning to detect and localize many objects from few examples

The current trend in object detection and localization is to learn predictions with high capacity deep neural networks trained on a very large amount of annotated data and using a high amount of processing power. In this work, we propose a new neural model which directly predicts bounding box coordinates. The particularity of our contribution lies in the local computations of predictions with a new form of local parameter sharing which keeps the overall amount of trainable parameters low. Key components of the model are spatial 2D-LSTM recurrent layers which convey contextual information between the regions of the image. We show that this model is more powerful than the state of the art in applications where training data is not as abundant as in the classical configuration of natural images and Imagenet/Pascal VOC tasks. We particularly target the detection of text in document images, but our method is not limited to this setting. The proposed model also facilitates the detection of many objects in a single image and can deal with inputs of variable sizes without resizing.
A critical overview of latest challenges and solutions of Mobile Cloud Computing

The unabated plethora of research activities to augment multifarious mobile devices by leveraging cloud resources have created new research field called Mobile Cloud Computing (MCC). Recently, researchers have found Mobile Cloud Computing (MCC) to be a promising venture combining the advantage of both the cloud and mobility in giving users available resources on the cloud wherever they are using their mobile phone. Despite its benefits, many challenges still remain with MCC. This paper addressed recent challenges ranging from limited computational capacity, connectivity, security, latency, and heterogeneity. Solutions for these challenges are suggested including mobile processes offloading, using HTML5 technologies to compensate for connectivity loss, SDN centralized control features for data security, utilizing the cloud for time consuming applications, and using cross platform applications neutralizes heterogeneous technologies. Finally, the paper concludes with an extracted list of new research idea for scholars to work on to further enhance MCC.
A Two-Level Investigation of Information Systems Outsourcing

O UTSOURCING is the contracting of various systems to outside information systems (IS) vendors. Ever since the Eastman Kodak--IBM partnership was reported in 1989 , outsourcing has emerged and has been recognized as a key method of managing IS. In previous studies of outsourcing [4, 5, 12, 15, 17, 20], two gaps are noticeable. The first relates to the fact that requirements for outsourcing are not uniform, and managers have different approaches to the process. Yet most previous research studies have phrased their inquiries unidimensionally in terms of the extent or degree of outsourcing: either as a binary variable or as size of contract in terms of the percentage of total IS budget [5, 15]. Neither unidimensional gauge is sufficiently expressive; neither allows representation of diverse patterns of outsourcing. Second, even though the IS industry had not used the term “outsourcing” explicitly in the past, outsourcing is not a new concept and has existed for many years in one form or another. Even though firms have repetitively used outside vendors for years, researchers have neither considered prior relationships in their studies of IS outsourcing nor studied the intentions of client firms to continue the partnership with the outsourcing vendors in the future. In this study, IS outsourcing decisions are investigated at two levels. The first level deals with the initial outsourcing decision of client firms. The second level pertains to the intention to continue the relationships with current outsourcing vendors in the future. The following three research questions are explored at these two levels (in contrast to the singlelevel approach taken by most researchers): • What are the dimensions of outsourcing decisions? Two dimensions, extent of substitution by vendors and strategic impact of IS applications, are proposed in order to conceptualize the diverse types of outsourcing relationships between clients and vendors. Based on these two dimensions, four types of outsourcing relationships are proposed. • What are the determinants that affect the dimensions of outsourcing decisions at the first level? The concepts derived from incomplete contracts [2, 9] and transactions cost economics [23, 24] theory are used with information technology (IT) organizational contexts and processes as a foundation to study the determinants of the two dimensions of outsourcing decisions.
What Is an Ontology?

The word “ontology” is used with different senses in different communities. The most radical difference is perhaps between the philosophical sense, which has of course a well-established tradition, and the computational sense, which emerged in the recent years in the knowledge engineering community, starting from an early informal definition of (computational) ontologies as “explicit specifications of conceptualizations”. In this paper we shall revisit the previous attempts to clarify and formalize such original definition, providing a detailed account of the notions of conceptualization and explicit specification, while discussing at the same time the importance of shared explicit specifications.
User-defined gestures for surface computing

Many surface computing prototypes have employed gestures created by system designers. Although such gestures are appropriate for early investigations, they are not necessarily reflective of user behavior. We present an approach to designing tabletop gestures that relies on eliciting gestures from non-technical users by first portraying the effect of a gesture, and then asking users to perform its cause. In all, 1080 gestures from 20 participants were logged, analyzed, and paired with think-aloud data for 27 commands performed with 1 and 2 hands. Our findings indicate that users rarely care about the number of fingers they employ, that one hand is preferred to two, that desktop idioms strongly influence users' mental models, and that some commands elicit little gestural agreement, suggesting the need for on-screen widgets. We also present a complete user-defined gesture set, quantitative agreement scores, implications for surface technology, and a taxonomy of surface gestures. Our results will help designers create better gesture sets informed by user behavior.
Scaling Reflection Prompts in Large Classrooms via Mobile Interfaces and Natural Language Processing

We present the iterative design, prototype, and evaluation of CourseMIRROR (Mobile In-situ Reflections and Review with Optimized Rubrics), an intelligent mobile learning system that uses natural language processing (NLP) techniques to enhance instructor-student interactions in large classrooms. CourseMIRROR enables streamlined and scaffolded reflection prompts by: 1) reminding and collecting students' in-situ written reflections after each lecture; 2) continuously monitoring the quality of a student's reflection at composition time and generating helpful feedback to scaffold reflection writing; and 3) summarizing the reflections and presenting the most significant ones to both instructors and students. Through a combination of a 60-participant lab study and eight semester-long deployments involving 317 students, we found that the reflection and feedback cycle enabled by CourseMIRROR is beneficial to both instructors and students. Furthermore, the reflection quality feedback feature can encourage students to compose more specific and higher-quality reflections, and the algorithms in CourseMIRROR are both robust to cold start and scalable to STEM courses in diverse topics.
Understanding predictability and exploration in human mobility

Predictive models for human mobility have important applications in many fields including traffic control, ubiquitous computing, and contextual advertisement. The predictive performance of models in literature varies quite broadly, from over 90% to under 40%. In this work we study which underlying factors - in terms of modeling approaches and spatio-temporal characteristics of the data sources - have resulted in this remarkably broad span of performance reported in the literature. Specifically we investigate which factors influence the accuracy of next-place prediction, using a high-precision location dataset of more than 400 users observed for periods between 3 months and one year. We show that it is much easier to achieve high accuracy when predicting the time-bin location than when predicting the next place. Moreover, we demonstrate how the temporal and spatial resolution of the data have strong influence on the accuracy of prediction. Finally we reveal that the exploration of new locations is an important factor in human mobility, and we measure that on average 20-25% of transitions are to new places, and approx. 70% of locations are visited only once. We discuss how these mechanisms are important factors limiting our ability to predict human mobility.
Followee recommendation based on text analysis of micro-blogging activity

Nowadays, more and more users keep up with news through information streams coming from real-time micro-blogging activity o ered by services such as Twitter. In these sites, information is shared via a followers/followees social network structure in which a follower will receive all the micro-blogs from his/her followees. Recent research e orts on understanding micro-blogging as a novel form of communication and news spreading medium, have identi ed three di erent categories of users in these systems: information sources, information seekers and friends. As the social network grows in the number of registered users, nding relevant and reliable users to receive interesting information becomes essential. In this paper we propose a followee recommender system based on the analysis of the content of micro-blogs to detect users' interests and in an exploration of the topology of follower/followee network to nd candidate users for recommendation. Experimental evaluation was conducted in order to determine the impact of di erent pro ling strategies based on the text analysis of micro-blogs as well as several factors that allows the identi cation of users acting as good information sources. We found that user-generated content available in the network is a rich source of information for pro ling users and nding like-minded people.