nils-herrmann/clean_scirepeval huggingface dataset:
Distributed Training Large-Scale Deep Architectures

Scale of data and scale of computation infrastructures together enable the current deep learning renaissance. However, training large-scale deep architectures demands both algorithmic improvement and careful system configuration. In this paper, we focus on employing the system approach to speed up large-scale training. Taking both the algorithmic and system aspects into consideration, we develop a procedure for setting mini-batch size and choosing computation algorithms. We also derive lemmas for determining the quantity of key components such as the number of GPUs and parameter servers. Experiments and examples show that these guidelines help effectively speed up large-scale deep learning training.
Rectangular Dielectric Resonator Antenna Array for 28 GHz Applications

In this paper, a Rectangular Dielectric Resonator Antenna (RDRA) with a modified feeding line is designed and investigated at 28GHz. The modified feed line is designed to excite the DR with relative permittivity of 10 which contributes to a wide bandwidth operation. The proposed single RDRA has been fabricated and mounted on a RT/Duroid 5880 (εr = 2.2 and tanδ = 0.0009) substrate. The optimized single element has been applied to array structure to improve the gain and achieve the required gain performance. The radiation pattern, impedance bandwidth and gain are simulated and measured accordingly. The number of elements and element spacing are studied for an optimum performance. The proposed antenna obtains a reflection coefficient response from 27.0GHz to 29.1GHz which cover the desired frequency band. This makes the proposed antenna achieve 2.1GHz impedance bandwidth and gain of 12.1 dB. Thus, it has potential for millimeter wave and 5G applications.
ST-GAN: Spatial Transformer Generative Adversarial Networks for Image Compositing

We address the problem of finding realistic geometric corrections to a foreground object such that it appears natural when composited into a background image. To achieve this, we propose a novel Generative Adversarial Network (GAN) architecture that utilizes Spatial Transformer Networks (STNs) as the generator, which we call Spatial Transformer GANs (ST-GANs). ST-GANs seek image realism by operating in the geometric warp parameter space. In particular, we exploit an iterative STN warping scheme and propose a sequential training strategy that achieves better results compared to naive training of a single generator. One of the key advantages of ST-GAN is its applicability to high-resolution images indirectly since the predicted warp parameters are transferable between reference frames. We demonstrate our approach in two applications: (1) visualizing how indoor furniture (e.g. from product images) might be perceived in a room, (2) hallucinating how accessories like glasses would look when matched with real portraits.
Building a Sentiment Summarizer for Local Service Reviews

Online user reviews are increasingly becoming the de-facto standard for measuring the quality of electronics, restaurants, merchants, etc. The sheer volume of online reviews makes it difficult for a human to process and extract all meaningful information in order to make an educated purchase. As a result, there has been a trend toward systems that can automatically summarize opinions from a set of reviews and display them in an easy to process manner [1, 9]. In this paper, we present a system that summarizes the sentiment of reviews for a local service such as a restaurant or hotel. In particular we focus on aspect-based summarization models , where a summary is built by extracting relevant aspects of a service, such as service or value, aggregating the sentiment per aspect, and selecting aspect-relevant text. We describe the details of both the aspect extraction and sentiment detection modules of our system. A novel aspect of these models is that they exploit user provided labels and domain specific characteristics of service reviews to increase quality.
Behavior recognition via sparse spatio-temporal features

A common trend in object recognition is to detect and leverage the use of sparse, informative feature points. The use of such features makes the problem more manageable while providing increased robustness to noise and pose variation. In this work we develop an extension of these ideas to the spatio-temporal case. For this purpose, we show that the direct 3D counterparts to commonly used 2D interest point detectors are inadequate, and we propose an alternative. Anchoring off of these interest points, we devise a recognition algorithm based on spatio-temporally windowed data. We present recognition results on a variety of datasets including both human and rodent behavior.
The Relationships among Brand Equity , Brand Preference , and Purchase Intention : Empirical Evidence from the Motorbike Market in Vietnam

This study is aimed at exploring the effects of components of brand equity on overall brand equity; and the effects of overall brand equity on brand preference and purchase intention in the motorbike market in Vietnam. Based on a sample of 309 consumers, structural equation modeling (SEM) is used to test hypotheses. The research reveals that: (1) perceived quality, brand association and brand loyalty have positive effects on overall brand equity, but brand awareness has no effect on overall brand equity; (2) overall brand equity has positive impacts on brand preference and purchase intention; and (3) brand preference has a positive influence on purchase intention. These findings have implications for marketers.
Strategies for increasing the operating frequency range of vibration energy harvesters : a review

This review presents possible strategies to increase the operational frequency range of vibration-based micro-generators. Most vibration-based micro-generators are spring-mass-damper systems which generate maximum power when the resonant frequency of the generator matches the frequency of the ambient vibration. Any difference between these two frequencies can result in a significant decrease in generated power. This is a fundamental limitation of resonant vibration generators which restricts their capability in real applications. Possible solutions include the periodic tuning of the resonant frequency of the generator so that it matches the frequency of the ambient vibration at all times or widening the bandwidth of the generator. Periodic tuning can be achieved using mechanical or electrical methods. Bandwidth widening can be achieved using a generator array, a mechanical stopper, nonlinear (e.g. magnetic) springs or bi-stable structures. Tuning methods can be classified into intermittent tuning (power is consumed periodically to tune the device) and continuous tuning (the tuning mechanism is continuously powered). This review presents a comprehensive review of the principles and operating strategies for increasing the operating frequency range of vibration-based micro-generators presented in the literature to date. The advantages and disadvantages of each strategy are evaluated and conclusions are drawn regarding the relevant merits of each approach.
Bolt: Anonymous Payment Channels for Decentralized Currencies

Bitcoin owes its success to the fact that transactions are transparently recorded in the blockchain, a global public ledger that removes the need for trusted parties. Unfortunately, recording every transaction in the blockchain causes privacy, latency, and scalability issues. Building on recent proposals for "micropayment channels" --- two party associations that use the ledger only for dispute resolution --- we introduce techniques for constructing anonymous payment channels. Our proposals allow for secure, instantaneous and private payments that substantially reduce the storage burden on the payment network. Specifically, we introduce three channel proposals, including a technique that allows payments via untrusted intermediaries. We build a concrete implementation of our scheme and show that it can be deployed via a soft fork to existing anonymous currencies such as ZCash.
Automated memory leak detection for production use

This paper presents Sniper, an automated memory leak detection tool for C/C++ production software. To track the staleness of allocated memory (which is a clue to potential leaks) with little overhead (mostly <3%), Sniper leverages instruction sampling using performance monitoring units available in commodity processors. It also offloads the time- and space-consuming analyses, and works on the original software without modifying the underlying memory allocator; it neither perturbs the application execution nor increases the heap size. The Sniper can even deal with multithreaded applications with very low overhead. In particular, it performs a statistical analysis, which views memory leaks as anomalies, for automated and systematic leak determination. Consequently, it accurately detected real-world memory leaks with no false positive, and achieved an F-measure of 81% on average for 17 benchmarks stress-tested with various memory leaks.
A FUZZY LOGIC BASED EXPERT SYSTEM FOR DETERMINATION OF HEALTH RISK LEVEL OF PATIENT

The aim of this study is to design a fuzzy expert system for calculating the health risk level of a patient. The fuzzy logic system is a simple, rule-based system and can be used to monitor biological systems that would be difficult or impossible to model with simple, linear mathematics. The designed system is based on the modified early warning score (MEWS).The system has 5 input field and 1 output field. The input fields are blood pressure, pulse rate, SPO2 ( it is an estimation of the oxygen saturation level in blood. ), temperature, and blood sugar. The output field refers the risk level of the patient. The output ranges from 0 to 14. This system uses Mamdani inference method. A larger value of output refers to greater degree of illness of the patient. This paper describes research results in the development of a fuzzy driven system to determine the risk levels of health for the patients. The implementation and simulation of the system is done using MATLAB fuzzy tool box.
Blockchain-based Smart Contracts: A Systematic Mapping Study

An appealing feature of blockchain technology is smart contracts. A smart contract is executable code that runs on top of the blockchain to facilitate, execute and enforce an agreement between untrusted parties without the involvement of a trusted third party. In this paper, we conduct a systematic mapping study to collect all research that is relevant to smart contracts from a technical perspective. The aim of doing so is to identify current research topics and open challenges for future studies in smart contract research. We extract 24 papers from different scientific databases. The results show that about two thirds of the papers focus on identifying and tackling smart contract issues. Four key issues are identified, namely, codifying, security, privacy and performance issues. The rest of the papers focuses on smart contract applications or other smart contract related topics. Research gaps that need to be addressed in future studies are provided.
The mathematics of cancer: integrating quantitative models

Mathematical modelling approaches have become increasingly abundant in cancer research. The complexity of cancer is well suited to quantitative approaches as it provides challenges and opportunities for new developments. In turn, mathematical modelling contributes to cancer research by helping to elucidate mechanisms and by providing quantitative predictions that can be validated. The recent expansion of quantitative models addresses many questions regarding tumour initiation, progression and metastases as well as intra-tumour heterogeneity, treatment responses and resistance. Mathematical models can complement experimental and clinical studies, but also challenge current paradigms, redefine our understanding of mechanisms driving tumorigenesis and shape future research in cancer biology.
Building Watson: An Overview of the DeepQA Project

by exploring new ways for computer technology to affect science, business, and society. Roughly three years ago, IBM Research was looking for a major research challenge to rival the scientific and popular interest of Deep Blue, the computer chess-playing champion (Hsu 2002), that also would have clear relevance to IBM business interests. With a wealth of enterprise-critical information being captured in natural language documentation of all forms, the problems with perusing only the top 10 or 20 most popular documents containing the user’s two or three key words are becoming increasingly apparent. This is especially the case in the enterprise where popularity is not as important an indicator of relevance and where recall can be as critical as precision. There is growing interest to have enterprise computer systems deeply analyze the breadth of relevant content to more precisely answer and justify answers to user’s natural language questions. We believe advances in question-answering (QA) technology can help support professionals in critical and timely decision making in areas like compliance, health care, business integrity, business intelligence, knowledge discovery, enterprise knowledge management, security, and customer support. For Articles
Effects of Size and Surface Charge of Polymeric Nanoparticles on in Vitro and in Vivo Applications

Biodegradable polymeric materials are the most common carriers for use in drug delivery systems. With this trend, newer drug delivery systems using targeted and controlled release polymeric nanoparticles (NPs) are being developed to manipulate their navigation in complex in vivo environment. However, a clear understanding of the interactions between biological systems and these nanoparticulates is still unexplored. Different studies have been performed to correlate the physicochemical properties of polymeric NPs with the biological responses. Size and surface charge are the two fundamental physicochemical properties that provide a key direction to design an effective NP formulation. In this critical review, our goal is to provide a brief overview on the influences of size and surface charge of different polymeric NPs in vitro and to highlight the challenges involved with in vivo trials.
A short portable mental status questionnaire for the assessment of organic brain deficit in elderly patients.

Clinicians whose practice includes elderly patients need a short, reliable instrument to detect the presence of intellectual impairment and to determine the degree. A 10-item Short Portable Mental Status Questionnaire (SPMSQ), easily administered by any clinician in the office or in a hospital, has been designed, tested, standardized and validated. The standardization and validation procedure included administering the test to 997 elderly persons residing in the community, to 141 elderly persons referred for psychiatric and other health and social problems to a multipurpose clinic, and to 102 elderly persons living in institutions such as nursing homes, homes for the aged, or state mental hospitals. It was found that educational level and race had to be taken into account in scoring individual performance. On the basis of the large community population, standards of performance were established for: 1) intact mental functioning, 2) borderline or mild organic impairment, 3) definite but moderate organic impairment, and 4) severe organic impairment. In the 141 clinic patients, the SPMSQ scores were correlated with the clinical diagnoses. There was a high level of agreement between the clinical diagnosis of organic brain syndrome and the SPMSQ scores that indicated moderate or severe organic impairment.
Social marketing campaigns and children's media use.

Media-related commercial marketing aimed at promoting the purchase of products and services by children, and by adults for children, is ubiquitous and has been associated with negative health consequences such as poor nutrition and physical inactivity. But, as Douglas Evans points out, not all marketing in the electronic media is confined to the sale of products. Increasingly savvy social marketers have begun to make extensive use of the same techniques and strategies used by commercial marketers to promote healthful behaviors and to counter some of the negative effects of conventional media marketing to children and adolescents. Evans points out that social marketing campaigns have been effective in helping to prevent and control tobacco use, increase physical activity, improve nutrition, and promote condom use, as well as other positive health behaviors. He reviews the evidence from a number of major recent campaigns and programming in the United States and overseas and describes the evaluation and research methods used to determine their effectiveness. He begins his review of the field of social marketing by describing how it uses many of the strategies practiced so successfully in commercial marketing. He notes the recent development of public health brands and the use of branding as a health promotion strategy. He then goes on to show how social marketing can promote healthful behavior, how it can counter media messages about unhealthful behavior, and how it can encourage discussions between parents and children. Evans concludes by noting some potential future applications to promote healthful media use by children and adolescents and to mitigate the effects of exposure to commercial marketing. These include adapting lessons learned from previous successful campaigns, such as delivering branded messages that promote healthful alternative behaviors. Evans also outlines a message strategy to promote "smart media use" to parents, children, and adolescents and suggests a brand based on personal interaction as a desirable alternative to "virtual interaction".
Locally adaptive dimensionality reduction for indexing large time series databases

Similarity search in large time series databases has attracted much research interest recently. It is a difficult problem because of the typically high dimensionality of the data. The most promising solutions involve performing dimensionality reduction on the data, then indexing the reduced data with a multidimensional index structure. Many dimensionality reduction techniques have been proposed, including Singular Value Decomposition (SVD), the Discrete Fourier transform (DFT), and the Discrete Wavelet Transform (DWT). In this article, we introduce a new dimensionality reduction technique, which we call Adaptive Piecewise Constant Approximation (APCA). While previous techniques (e.g., SVD, DFT and DWT) choose a common representation for all the items in the database that minimizes the global reconstruction error, APCA approximates each time series by a set of constant value segments of varying lengths such that their individual reconstruction errors are minimal. We show how APCA can be indexed using a multidimensional index structure. We propose two distance measures in the indexed space that exploit the high fidelity of APCA for fast searching: a lower bounding Euclidean distance approximation, and a non-lower-bounding, but very tight, Euclidean distance approximation, and show how they can support fast exact searching and even faster approximate searching on the same index structure. We theoretically and empirically compare APCA to all the other techniques and demonstrate its superiority.
Smart meters for power grid — Challenges, issues, advantages and status

Smart meter is an advanced energy meter that measures consumption of electrical energy providing additional information compared to a conventional energy meter. Integration of smart meters into electricity grid involves implementation of a variety of techniques and software, depending on the features that the situation demands. Design of a smart meter depends on the requirements of the utility company as well as the customer. This paper discusses various features and technologies that can be integrated with a smart meter. In fact, deployment of smart meters needs proper selection and implementation of a communication network satisfying the security standards of smart grid communication. This paper outlines various issues and challenges involved in design, deployment, utilization, and maintenance of the smart meter infrastructure. In addition, several applications and advantages of smart meter, in the view of future electricity market are discussed in detail. This paper explains the importance of introducing smart meters in developing countries. In addition, the status of smart metering in various countries is also illustrated.
Label-Embedding for Image Classification

Attributes act as intermediate representations that enable parameter sharing between classes, a must when training data is scarce. We propose to view attribute-based image classification as a label-embedding problem: each class is embedded in the space of attribute vectors. We introduce a function that measures the compatibility between an image and a label embedding. The parameters of this function are learned on a training set of labeled samples to ensure that, given an image, the correct classes rank higher than the incorrect ones. Birds datasets show that the proposed framework outperforms the standard Direct Attribute Prediction baseline in a zero-shot learning scenario. Label embedding enjoys a built-in ability to leverage alternative sources of information instead of or in addition to attributes, such as, e.g., class hierarchies or textual descriptions. Moreover, label embedding encompasses the whole range of learning settings from zero-shot learning to regular learning with a large number of labeled examples.
Teacher self-efficacy and teacher burnout : A study of relations

In any teaching and learning setting, there are some variables that play a highly significant role in both teachers’ and learners’ performance. Two of these influential psychological domains in educational context include self-efficacy and burnout. This study is conducted to investigate the relationship between the self-efficacy of Iranian teachers of English and their reports of burnout. The data was collected through application of two questionnaires. The Maslach Burnout Inventory (MBI; Maslach& Jackson 1981, 1986) and Teacher Efficacy Scales (Woolfolk& Hoy, 1990) were administered to ten university teachers. After obtaining the raw data, the SPSS software (version 16) was used to change the data into numerical interpretable forms. In order to determine the relationship between self-efficacy and teachers’ burnout, correlational analysis was employed. The results showed that participants’ self-efficacy has a reverse relationship with their burnout.
Massively Multilingual Word Embeddings

We introduce new methods for estimating and evaluating embeddings of words from dozens of languages in a single shared embedding space. Our estimation methods, multiCluster and multiCCA, use dictionaries and monolingual data; they do not require parallel data. Our new evaluation method, multi QVEC+, is shown to correlate better than previous ones with two downstream tasks (text categorization and parsing). On this evaluation and others, our estimation methods outperform existing ones. We also describe a web portal for evaluation that will facilitate further research in this area, along with open-source releases of all our methods.