nils-herrmann/clean_scirepeval huggingface dataset:
ASPIRE: exploiting asynchronous parallelism in iterative algorithms using a relaxed consistency based DSM

Many vertex-centric graph algorithms can be expressed using asynchronous parallelism by relaxing certain read-after-write data dependences and allowing threads to compute vertex values using stale (i.e., not the most recent) values of their neighboring vertices. We observe that on distributed shared memory systems, by converting synchronous algorithms into their asynchronous counterparts, algorithms can be made tolerant to high inter-node communication latency. However, high inter-node communication latency can lead to excessive use of stale values causing an increase in the number of iterations required by the algorithms to converge. Although by using bounded staleness we can restrict the slowdown in the rate of convergence, this also restricts the ability to tolerate communication latency. In this paper we design a relaxed memory consistency model and consistency protocol that simultaneously tolerate communication latency and minimize the use of stale values. This is achieved via a coordinated use of best effort refresh policy and bounded staleness. We demonstrate that for a range of asynchronous graph algorithms and PDE solvers, on an average, our approach outperforms algorithms based upon: prior relaxed memory models that allow stale values by at least 2.27x; and Bulk Synchronous Parallel (BSP) model by 4.2x. We also show that our approach frequently outperforms GraphLab, a popular distributed graph processing framework.
Measuring e-Commerce Success: Applying the DeLone & McLean Information Systems Success Model

Information technology and the Internet have had a dramatic effect on business operations. Companies are making large investments in e-commerce applications but are hard pressed to evaluate the success of their e-commerce systems. The DeLone & McLean Information Systems Success Model can be adapted to the measurement challenges of the new e-commerce world. The six dimensions of the updated model are a parsimonious framework for organizing the e-commerce success metrics identified in the literature. Two case examples demonstrate how the model can be used to guide the identification and specification of e-commerce success metrics.
Supervised Distributional Hypernym Discovery via Domain Adaptation

➔ Apply this matrix to an unseen domain-specific term, so that the resulting vector constitutes the “ideal” hypernym for that term. Since it may not coincide with any predefined vector, retrieve its nearest neighbours by cosine similarity. Supervised Distributional Hypernym Discovery via Domain Adaptation Luis Espinosa-Anke1,3, Jose Camacho-Collados2, Claudio Delli Bovi2, Horacio Saggion1 1TALN Group, Department of Information and Communication Technologies 2Department of Computer Science, Sapienza University of Rome 3Savanamed {luis.espinosa, ,
A three-phase ZVS PWM DC/DC converter with asymmetrical duty cycle for high power applications

This paper proposes the application of the asymmetrical duty cycle to the three-phase dc/dc pulse-width modulation isolated converter. Thus, soft commutation is achieved for a wide load range using the leakage inductance of the transformer and the intrinsic capacitance of the switches, as no additional semiconductor devices are needed. The resulting topology is characterized by an increase in the input current and output current frequency, by a factor of three compared to the full-bridge converter, which reduces the filters size. In addition, the rms current through the power components is lower, implying the improved thermal distribution of the losses. Besides, the three-phase transformer allows the reduction of the core size. In this paper, a mathematical analysis, the main waveforms, a design procedure, as well as simulation and experimental results obtained in a prototype of 6 kW are presented.
A multi-frame image super-resolution method

Multi-frame image super-resolution (SR) aims to utilize information from a set of lowresolution (LR) images to compose a high-resolution (HR) one. As it is desirable or essential in many real applications, recent years have witnessed the growing interest in the problem of multi-frame SR reconstruction. This set of algorithms commonly utilizes a linear observation model to construct the relationship between the recorded LR images to the unknown reconstructed HR image estimates. Recently, regularizationbased schemes have been demonstrated to be effective because SR reconstruction is actually an ill-posed problem. Working within this promising framework, this paper first proposes two new regularization items, termed as locally adaptive bilateral total variation and consistency of gradients, to keep edges and flat regions, which are implicitly described in LR images, sharp and smooth, respectively. Thereafter, the combination of the proposed regularization items is superior to existing regularization items because it considers both edges and flat regions while existing ones consider only edges. Thorough experimental results show the effectiveness of the new algorithm for SR reconstruction. & 2009 Elsevier B.V. All rights reserved.
Continuous Hyper-parameter Learning for Support Vector Machines

In this paper, we address the problem of determining optimal hyper-parameters for support vector machines (SVMs). The standard way for solving the model selection problem is to use grid search. Grid search constitutes an exhaustive search over a pre-defined discretized set of possible parameter values and evaluating the cross-validation error until the best is found. We developed a bi-level optimization approach to solve the model selection problem for linear and kernel SVMs, including the extension to learn several kernel parameters. Using this method, we can overcome the discretization of the parameter space using continuous optimization, and the complexity of the method only increases linearly with the number of parameters (instead of exponentially using grid search). In experiments, we determine optimal hyper-parameters based on different smooth estimates of the cross-validation error and find that only very few iterations of bi-level optimization yield good classification rates.
Exploring Question Understanding and Adaptation in Neural-Network-Based Question Answering

The last several years have seen intensive interest in exploring neural-networkbased models for machine comprehension (MC) and question answering (QA). In this paper, we approach the problems by closely modelling questions in a neural network framework. We first introduce syntactic information to help encode questions. We then view and model different types of questions and the information shared among them as an adaptation task and proposed adaptation models for them. On the Stanford Question Answering Dataset (SQuAD), we show that these approaches can help attain better results over a competitive baseline.
Sentence-level Sentiment Classification with Weak Supervision

Sentence-level sentiment classification is important to understand users' fine-grained opinions. Existing methods for sentence-level sentiment classification are mainly based on supervised learning. However, it is difficult to obtain sentiment labels of sentences since manual annotation is expensive and time-consuming. In this paper, we propose an approach for sentence-level sentiment classification without the need of sentence labels. More specifically, we propose a unified framework to incorporate two types of weak supervision, i.e., document-level and word-level sentiment labels, to learn the sentence-level sentiment classifier. In addition, the contextual information of sentences and words extracted from unlabeled sentences is incorporated into our approach to enhance the learning of sentiment classifier. Experiments on benchmark datasets show that our approach can effectively improve the performance of sentence-level sentiment classification.
Distributed Algorithms for Topic Models

We describe distributed algorithms for two widely-used topic models, namely the Latent Dirichlet Allocation (LDA) model, and the Hierarchical Dirichet Process (HDP) model. In our distributed algorithms the data is partitioned across separate processors and inference is done in a parallel, distributed fashion. We propose two distributed algorithms for LDA. The first algorithm is a straightforward mapping of LDA to a distributed processor setting. In this algorithm processors concurrently perform Gibbs sampling over local data followed by a global update of topic counts. The algorithm is simple to implement and can be viewed as an approximation to Gibbs-sampled LDA. The second version is a model that uses a hierarchical Bayesian extension of LDA to directly account for distributed data. This model has a theoretical guarantee of convergence but is more complex to implement than the first algorithm. Our distributed algorithm for HDP takes the straightforward mapping approach, and merges newly-created topics either by matching or by topic-id. Using five real-world text corpora we show that distributed learning works well in practice. For both LDA and HDP, we show that the converged test-data log probability for distributed learning is indistinguishable from that obtained with single-processor learning. Our extensive experimental results include learning topic models for two multi-million document collections using a 1024-processor parallel computer.
SoftAir: A software defined networking architecture for 5G wireless systems

One of the main building blocks and major challenges for 5G cellular systems is the design of flexible network architectures which can be realized by the software defined networking paradigm. Existing commercial cellular systems rely on closed and inflexible hardware-based architectures both at the radio frontend and in the core network. These problems significantly delay the adoption and deployment of new standards, impose significant challenges in implementing and innovation of new techniques to maximize the network capacity and accordingly the coverage, and prevent provisioning of trulydifferentiated services which are able to adapt to growing and uneven and highly variable traffic patterns. In this paper, a new softwaredefined architecture, called SoftAir, for next generation (5G) wireless systems, is introduced. Specifically, the novel ideas of network function cloudification and network virtualization are exploited to provide a scalable, flexible and resilient network architecture. Moreover, the essential enabling technologies to support and manage the proposed architecture are discussed in details, including fine-grained base station decomposition, seamless incorporation of Openflow, mobilityaware control traffic balancing, resource-efficient network virtualization, and distributed and collaborative traffic classification. Furthermore, the major benefits of SoftAir architecture with its enabling technologies are showcased by introducing softwaredefined traffic engineering solutions. The challenging issues for realizing SoftAir are also discussed in details.
A new cuffless optical sensor for blood pressure measuring with self-adaptive signal processing

A new cuffless, wireless optical blood pressure (BP) sensor is successfully developed by this study. This device employs the technique of photoplethysmograph (PPG) to read out in real time the intravascular blood volume change and then to calculate BP. The system utilizes a red-light LED with a wavelengths of 660 nm. The readout circuit includes a pre-amplifier, a band-pass filter, a programmable gain amplifier (PGA), an Arduino unit for calculation and a Bluetooth module for wireless communication. A notebook or a mobile phone is also used to display continuous BPs and conducts statistical analysis/results. The passband is from 0.3 to 3.4 Hz. The range of adjustable gain are from 60 to 80 dB. Measured PPGs of different subjects can be auto-adjusted with varied gain by a PGA with a pre-designed digital circuit. In results, the signal-to-noise ratio (SNR) is improved for accurate estimates on BPs. 8 subjects participated in the experimental validation, in which the obtained BPs are compared with the results from a commercial blood pressure monitor by OMRON. The maximum error of experimental results is ± 6 mmHg, which is less than 8 mmHg conforming to the requirement by the Advancement of Medical Instrumentation (AAMI). This study pioneers to report stable, accurate estimates on BPs by a cuffless optical hand-held device.
Automatic Planning of Manipulator Transfer Movements

The class of problems that involve finding where to place or how to move a solid object in the presence of obstacles is discussed. The solution to this class of problems is essential to the automatic planning of manipulator transfer movements, i.e., the motions to grasp a part and place it at some destination. For example, planning transfer movements requires the ability to plan paths for the manipulator that avoid collisions with objects in the workspace and the ability to choose safe grasp points on objects. The approach to these problems described here is based on a method of computing an explicit representation of the manipulator configurations that would bring about a collision.
Visual Search for Change : A Probe into the Nature of Attentional Processing

A set of visual search experiments tested the proposal that focused attention is needed to detect change. Displays were arrays of rectangles, with the target being the item that continually changed its orientation or contrast polarity. Five aspects of performance were examined: linearity of response, processing time, capacity, selectivity, and memory trace. Detection of change was found to be a self-terminating process requiring a time that increased linearly with the number of items in the display. Capacity for orientation was found to be about five items, a value comparable to estimates of attentional capacity. Observers were able to filter out both static and dynamic variations in irrelevant properties. Analysis also indicated a memory for previously attended locations. These results support the hypothesis that the process needed to detect change is much the same as the attentional process needed to detect complex static patterns. Interestingly, the features of orientation and polarity were found to be handled in somewhat different ways. Taken together, these results not only provide evidence that focused attention is needed to see change, but also show that change detection itself can provide new insights into the nature of attentional processing.
Exploring Miner Evolution in Bitcoin Network

In recent years, Bitcoin, a peer-to-peer network based crypto digital currency, has attracted a lot of attentions from the media, the academia, and the general public. A user in Bitcoin network can create Bitcoins by packing and verifying new transactions in the network using their computation power. Driven by the price surge of Bitcoin, users are increasingly investing on expensive specialized hardware for Bitcoin mining. To obtain steady payouts, users also pool their computation resources to conduct pool mining. In this paper, we study the evolution of Bitcoin miners by analyzing the complete transaction blockchain. We characterize how the productivity, computation power and transaction activity of miners evolve over time. We also conduct an in-depth study on the largest mining pool F2Pool. We show how it grows over time and how computation power is distributed among its miners. Finally, we build a simple economic model to explain the evolution of Bitcoin miners.
VoxelPipe: A Programmable Pipeline for 3D Voxelization

We present a highly flexible and efficient software pipeline for programmable triangle voxelization. The pipeline, entirely written in CUDA, supports both fully conservative and thin voxelizations, multiple boolean, floating point, vector-typed render targets, user-defined vertex and fragment shaders, and a bucketing mode which can be used to generate 3D A-buffers containing the entire list of fragments belonging to each voxel. For maximum efficiency, voxelization is implemented as a sort-middle tile-based rasterizer, while the A-buffer mode, essentially performing 3D binning of triangles over uniform grids, uses a sort-last pipeline. Despite its major flexibility, the performance of our tile-based rasterizer is always competitive with and sometimes more than an order of magnitude superior to that of state-of-the-art binary voxelizers, whereas our bucketing system is up to 4 times faster than previous implementations. In both cases the results have been achieved through the use of careful load-balancing and high performance sorting primitives.
Developing a telepresence robot for interpersonal communication with the elderly in a home environment.

"Telepresence" is an interesting field that includes virtual reality implementations with human-system interfaces, communication technologies, and robotics. This paper describes the development of a telepresence robot called Telepresence Robot for Interpersonal Communication (TRIC) for the purpose of interpersonal communication with the elderly in a home environment. The main aim behind TRIC's development is to allow elderly populations to remain in their home environments, while loved ones and caregivers are able to maintain a higher level of communication and monitoring than via traditional methods. TRIC aims to be a low-cost, lightweight robot, which can be easily implemented in the home environment. Under this goal, decisions on the design elements included are discussed. In particular, the implementation of key autonomous behaviors in TRIC to increase the user's capability of projection of self and operation of the telepresence robot, in addition to increasing the interactive capability of the participant as a dialogist are emphasized. The technical development and integration of the modules in TRIC, as well as human factors considerations are then described. Preliminary functional tests show that new users were able to effectively navigate TRIC and easily locate visual targets. Finally the future developments of TRIC, especially the possibility of using TRIC for home tele-health monitoring and tele-homecare visits are discussed.
A Split-merge Markov Chain Monte Carlo Procedure for the Dirichlet Process Mixture Model

We propose a split-merge Markov chain algorithm to address the problem of inee-cient sampling for conjugate Dirichlet process mixture models. Traditional Markov chain Monte Carlo methods for Bayesian mixture models, such as Gibbs sampling, can become trapped in isolated modes corresponding to an inappropriate clustering of data points. This article describes a Metropolis-Hastings procedure that can escape such local modes by splitting or merging mixture components. Our Metropolis-Hastings algorithm employs a new technique in which an appropriate proposal for splitting or merging components is obtained by using a restricted Gibbs sampling scan. We demonstrate empirically that our method outperforms the Gibbs sampler in situations where two or more components are similar in structure.
Evaluation of Parallel Coordinates: Overview, Categorization and Guidelines for Future Research

The parallel coordinates technique is widely used for the analysis of multivariate data. During recent decades significant research efforts have been devoted to exploring the applicability of the technique and to expand upon it, resulting in a variety of extensions. Of these many research activities, a surprisingly small number concerns user-centred evaluations investigating actual use and usability issues for different tasks, data and domains. The result is a clear lack of convincing evidence to support and guide uptake by users as well as future research directions. To address these issues this paper contributes a thorough literature survey of what has been done in the area of user-centred evaluation of parallel coordinates. These evaluations are divided into four categories based on characterization of use, derived from the survey. Based on the data from the survey and the categorization combined with the authors' experience of working with parallel coordinates, a set of guidelines for future research directions is proposed.
Trusted Execution Environment: What It is, and What It is Not

Nowadays, there is a trend to design complex, yet secure systems. In this context, the Trusted Execution Environment (TEE) was designed to enrich the previously defined trusted platforms. TEE is commonly known as an isolated processing environment in which applications can be securely executed irrespective of the rest of the system. However, TEE still lacks a precise definition as well as representative building blocks that systematize its design. Existing definitions of TEE are largely inconsistent and unspecific, which leads to confusion in the use of the term and its differentiation from related concepts, such as secure execution environment (SEE). In this paper, we propose a precise definition of TEE and analyze its core properties. Furthermore, we discuss important concepts related to TEE, such as trust and formal verification. We give a short survey on the existing academic and industrial ARM TrustZone-based TEE, and compare them using our proposed definition. Finally, we discuss some known attacks on deployed TEE as well as its wide use to guarantee security in diverse applications.
Preference for Online Social Interaction A Theory of Problematic Internet Use and Psychosocial Well-Being

The model introduced and tested in the current study suggests that lonely and depressed individuals may develop a preference for online social interaction, which, in turn, leads to negative outcomes associated with their Internet use. Participants completed measures of preference for online social interaction, depression, loneliness, problematic Internet use, and negative outcomes resulting from their Internet use. Results indicated that psychosocial health predicted levels of preference for online social interaction, which, in turn, predicted negative outcomes associated with problematic Internet use. In addition, the results indicated that the influence of psychosocial distress on negative outcomes due to Internet use is mediated by preference for online socialization and other symptoms of problematic Internet use. The results support the current hypothesis that that individuals’ preference for online, rather than face-to-face, social interaction plays an important role in the development of negative consequences associated with problematic Internet use.
Neural Tensor Factorization

Neural collaborative filtering (NCF)  and recurrent recommender systems (RRN) [38] have been successful in modeling user-item relational data. However, they are also limited in their assumption of static or sequential modeling of relational data as they do not account for evolving users’ preference over time as well as changes in the underlying factors that drive the change in user-item relationship over time. We address these limitations by proposing a Neural network based Tensor Factorization (NTF) model for predictive tasks on dynamic relational data. The NTF model generalizes conventional tensor factorization from two perspectives: First, it leverages the long short-term memory architecture to characterize the multi-dimensional temporal interactions on relational data. Second, it incorporates the multi-layer perceptron structure for learning the non-linearities between different latent factors. Our extensive experiments demonstrate the significant improvement in rating prediction and link prediction on dynamic relational data by our NTF model over both neural network based factorization models and other traditional methods.  Xian Wu, Baoxu Shi, Yuxiao Dong, Chao Huang, Nitesh V. Chawla. 2018. Neural Tensor Factorization. In Proceedings of International Conference of Knowledge Discovery and Data Mining (KDD’18). ACM, New York, NY, USA, 10 pages.