nils-herrmann/clean_scirepeval huggingface dataset:
Enhancing promotional strategies within social marketing programs: use of Web 2.0 social media.

The second generation of Internet-based applications (i.e., Web 2.0), in which users control communication, holds promise to significantly enhance promotional efforts within social marketing campaigns. Web 2.0 applications can directly engage consumers in the creative process by both producing and distributing information through collaborative writing, content sharing, social networking, social bookmarking, and syndication. Web 2.0 can also enhance the power of viral marketing by increasing the speed at which consumers share experiences and opinions with progressively larger audiences. Because of the novelty and potential effectiveness of Web 2.0, social marketers may be enticed to prematurely incorporate related applications into promotional plans. However, as strategic issues such as priority audience preferences, selection of appropriate applications, tracking and evaluation, and related costs are carefully considered, Web 2.0 will expand to allow health promotion practitioners more direct access to consumers with less dependency on traditional communication channels.
Coexistence of WiFi and LiFi toward 5G: concepts, opportunities, and challenges

Smart phones, tablets, and the rise of the Internet of Things are driving an insatiable demand for wireless capacity. This demand requires networking and Internet infrastructures to evolve to meet the needs of current and future multimedia applications. Wireless HetNets will play an important role toward the goal of using a diverse spectrum to provide high quality-of-service, especially in indoor environments where most data are consumed. An additional tier in the wireless HetNets concept is envisioned using indoor gigabit small-cells to offer additional wireless capacity where it is needed the most. The use of light as a new mobile access medium is considered promising. In this article, we describe the general characteristics of WiFi and VLC (or LiFi) and demonstrate a practical framework for both technologies to coexist. We explore the existing research activity in this area and articulate current and future research challenges based on our experience in building a proof-of-concept prototype VLC HetNet.
Battery health management system for electric UAVs

This paper presents a novel battery health management system for electric UAVs (unmanned aerial vehicles) based on a Bayesian inference driven prognostic framework. The aim is to be able to predict the end-of-discharge (EOD) event that indicates that the battery pack has run out of charge for any given flight of an electric UAV platform. The amount of usable charge of a battery for a given discharge profile is not only dependent on the starting state-of-charge (SOC), but also other factors like battery health and the discharge or load profile imposed. This problem is more pronounced in battery powered electric UAVs since different flight regimes like takeoff/landing and cruise have different power requirements and a dead stick condition (battery shut off in flight) can have catastrophic consequences. Since UAVs deployments are relatively new, there is a lack of statistically significant flight data to motivate data-driven approaches. Consequently, we have developed a detailed discharge model for the batteries used and used it in a Bayesian inference based filtering (Particle Filtering) technique to generate remaining useful life (RUL) distributions for a given discharge. The results section presents the validation of this approach in hardware-in-the-loop tests.12
The callosal angle measured on MRI as a predictor of outcome in idiopathic normal-pressure hydrocephalus.

OBJECT
Different neuroimaging biomarkers have been studied to find a tool for prediction of response to CSF shunting in idiopathic normal-pressure hydrocephalus (iNPH). The callosal angle (CA) has been described as useful in discriminating iNPH from ventricular dilation secondary to atrophy. However, the usefulness of the CA as a prognostic tool for the selection of shunt candidates among patients with iNPH is unclear. The aim of this study was to compare the CA in shunt responders with that in nonresponders and clarify whether the CA can serve as a predictor of the outcome.



Preoperative MRI brain scans were evaluated in 109 patients who had undergone shunt surgery for iNPH during 2006-2010. Multiplanar reconstruction was performed interactively to obtain a coronal image through the posterior commissure, perpendicular to the anterior-posterior commissure plane. The CA was measured as the angle between the lateral ventricles on the coronal image. The patients were examined clinically before surgery and at 12 months postoperatively.



Shunt responders had a significantly smaller mean preoperative CA compared with nonresponders: 59° (95% CI 56°-63°) versus 68° (95% CI 61°-75°) (p < 0.05). A CA cutoff value of 63° showed the best prognostic accuracy.



The preoperative CA is smaller in patients whose condition improves after shunt surgery and may be a useful tool in the selection of shunt candidates among patients with iNPH.
Accelerometer-based control of an industrial robotic arm

Most of industrial robots are still programmed using the typical teaching process, through the use of the robot teach pendant. In this paper is proposed an accelerometer-based system to control an industrial robot using two low-cost and small 3-axis wireless accelerometers. These accelerometers are attached to the human arms, capturing its behavior (gestures and postures). An Artificial Neural Network (ANN) trained with a back-propagation algorithm was used to recognize arm gestures and postures, which then will be used as input in the control of the robot. The aim is that the robot starts the movement almost at the same time as the user starts to perform a gesture or posture (low response time). The results show that the system allows the control of an industrial robot in an intuitive way. However, the achieved recognition rate of gestures and postures (92%) should be improved in future, keeping the compromise with the system response time (160 milliseconds). Finally, the results of some tests performed with an industrial robot are presented and discussed.
Technological Forecasting – A Review

This report aims to summarize the field of technological forecasting (TF), its techniques and applications by considering the following questions: • What are the purposes of TF? • Which techniques are used for TF? • What are the strengths and weaknesses of these techniques / how do we evaluate their quality? • Do we need different TF techniques for different purposes/technologies? We also present a brief analysis of how TF is used in practice. We analyze how corporate decisions, such as investing millions of dollars to a new technology like solar energy, are being made and we explore if funding allocation decisions are based on “objective, repeatable, and quantifiable” decision parameters. Throughout the analysis, we compare the bibliometric and semantic-enabled approach of the MIT/MIST Collaborative research project “Technological Forecasting using Data Mining and Semantics” (TFDMS) with the existing studies / practices of TF and where TFDMS fits in and how it will contribute to the general TF field.
Detecting Sensitive Information from Textual Documents: An Information-Theoretic Approach

Whenever a document containing sensitive information needs to be made public, privacy-preserving measures should be implemented. Document sanitization aims at detecting sensitive pieces of information in text, which are removed or hidden prior publication. Even though methods detecting sensitive structured information like e-mails, dates or social security numbers, or domain specific data like disease names have been developed, the sanitization of raw textual data has been scarcely addressed. In this paper, we present a general-purpose method to automatically detect sensitive information from textual documents in a domain-independent way. Relying on the Information Theory and a corpus as large as the Web, it assess the degree of sensitiveness of terms according to the amount of information they provide. Preliminary results show that our method significantly improves the detection recall in comparison with approaches based on trained classifiers.
Optimal multimodal fusion for multimedia data analysis

Considerable research has been devoted to utilizing multimodal features for better understanding multimedia data. However, two core research issues have not yet been adequately addressed. First, given a set of features extracted from multiple media sources (e.g., extracted from the visual, audio, and caption track of videos), how do we determine the best modalities? Second, once a set of modalities has been identified, how do we best fuse them to map to semantics? In this paper, we propose a two-step approach. The first step finds <i>statistically independent modalities</i> from raw features. In the second step, we use <i>super-kernel fusion</i> to determine the optimal combination of individual modalities. We carefully analyze the tradeoffs between three design factors that affect fusion performance: <i>modality independence</i>, <i>curse of dimensionality</i>, and <i>fusion-model complexity</i>. Through analytical and empirical studies, we demonstrate that our two-step approach, which achieves a careful balance of the three design factors, can improve class-prediction accuracy over traditional techniques.
State of the art in statistical methods for language and speech processing

Recent years have seen rapid growth in the deployment of statistical methods for computational language and speech processing. he current popularity of such methods can be traced to the convergence of several factors, including the increasing amount of ata now accessible, sustained advances in computing power and storage capabilities, and ongoing improvements in machine earning algorithms. The purpose of this contribution is to review the state of the art in both areas, point out the top trends in tatistical modelling across a wide range of problems, and identify their most salient characteristics. The paper concludes with some rognostications regarding the likely impact on the field going forward.
FlashCache: a NAND flash memory file cache for low power web servers

We propose an architecture that uses NAND flash memory to reduce main memory power in web server platforms. Our architecture uses a two level file buffer cache composed of a relatively small DRAM, which includes a primary file buffer cache, and a flash memory secondary file buffer cache. Compared to a conventional DRAM-only architecture, our architecture consumes orders of magnitude less idle power while remaining cost effective. This is a result of using flash memory, which consumes orders of magnitude less idle power than DRAM and is twice as dense. The client request behavior in web servers, allows us to show that the primary drawbacks of flash memory?endurance and long write latencies?can easily be overcome. In fact the wear-level aware management techniques that we propose are not heavily used.
From Cyber Security Information Sharing to Threat Management

Across the world, organizations have teams gathering threat data to protect themselves from incoming cyber attacks and maintain a strong cyber security posture. Teams are also sharing information, because along with the data collected internally, organizations need external information to have a comprehensive view of the threat landscape. The information about cyber threats comes from a variety of sources, including sharing communities, open-source and commercial sources, and it spans many different levels and timescales. Immediately actionable information are often low-level indicators of compromise, such as known malware hash values or command-and-control IP addresses, where an actionable response can be executed automatically by a system. Threat intelligence refers to more complex cyber threat information that has been acquired or inferred through the analysis of existing information. Information such as the different malware families used over time with an attack or the network of threat actors involved in an attack, is valuable information and can be vital to understanding and predicting attacks, threat developments, as well as informing law enforcement investigations. This information is also actionable, but on a longer time scale. Moreover, it requires action and decision-making at the human level. There is a need for effective intelligence management platforms to facilitate the generation, refinement, and vetting of data, post sharing. In designing such a system, some of the key challenges that exist include: working with multiple intelligence sources, combining and enriching data for greater intelligence, determining intelligence relevance based on technical constructs, and organizational input, delivery into organizational workflows and into technological products. This paper discusses these challenges encountered and summarizes the community requirements and expectations for an all-encompassing Threat Intelligence Management Platform. The requirements expressed in this paper, when implemented, will serve as building blocks to create systems that can maximize value out of a set of collected intelligence and translate those findings into action for a broad range of stakeholders.
Swoogle: Searching for Knowledge on the Semantic Web

Most knowledge on the Web is encoded as natural language text, which is convenient for human users but very difficult for software agents to understand. Even with increased use of XML-encoded information, software agents still need to process the tags and literal symbols using application dependent semantics. The Semantic Web offers an approach in which knowledge can be published by and shared among agents using symbols with a well defined, machine-interpretable semantics. The Semantic Web is a “web of data” in that (i) both ontologies and instance data are published in a distributed fashion; (ii) symbols are either ‘literals’ or universally addressable ‘resources’ (URI references) each of which comes with unique semantics; and (iii) information is semi-structured. The Friend-of-a-Friend (FOAF) project (http://www.foafproject.org/) is a good application of the Semantic Web in which users publish their personal profiles by instantiating the foaf:Personclass and adding various properties drawn from any number of ontologies. The Semantic Web’s distributed nature raises significant data access problems – how can an agent discover, index, search and navigate knowledge on the Semantic Web? Swoogle (Dinget al. 2004) was developed to facilitate webscale semantic web data access by providing these services to both human and software agents. It focuses on two levels of knowledge granularity: URI based semantic web vocabulary andsemantic web documents (SWDs), i.e., RDF and OWL documents encoded in XML, NTriples or N3. Figure 1 shows Swoogle’s architecture. The discovery component automatically discovers and revisits SWDs using a set of integrated web crawlers. The digest component computes metadata for SWDs and semantic web terms (SWTs) as well as identifies relations among them, e.g., “an SWD instantiates an SWT class”, and “an SWT class is the domain of an SWT property”. The analysiscomponent uses cached SWDs and their metadata to derive analytical reports, such as classifying ontologies among SWDs and ranking SWDs by their importance. The s rvicecomponent sup-
A comparison of methods to test mediation and other intervening variable effects.

A Monte Carlo study compared 14 methods to test the statistical significance of the intervening variable effect. An intervening variable (mediator) transmits the effect of an independent variable to a dependent variable. The commonly used R. M. Baron and D. A. Kenny (1986) approach has low statistical power. Two methods based on the distribution of the product and 2 difference-in-coefficients methods have the most accurate Type I error rates and greatest statistical power except in 1 important case in which Type I error rates are too high. The best balance of Type I error and statistical power across all cases is the test of the joint significance of the two effects comprising the intervening variable effect.
Machine learning model for sign language interpretation using webcam images

Human beings interact with each other either using a natural language channel such as words, writing, or by body language (gestures) e.g. hand gestures, head gestures, facial expression, lip motion and so on. As understanding natural language is important, understanding sign language is also very important. The sign language is the basic communication method within hearing disable people. People with hearing disabilities face problems in communicating with other hearing people without a translator. For this reason, the implementation of a system that recognize the sign language would have a significant benefit impact on deaf people social live. In this paper, we have proposed a marker-free, visual Indian Sign Language recognition system using image processing, computer vision and neural network methodologies, to identify the characteristics of the hand in images taken from a video trough web camera. This approach will convert video of daily frequently used full sentences gesture into a text and then convert it into audio. Identification of hand shape from continuous frames will be done by using series of image processing operations. Interpretation of signs and corresponding meaning will be identified by using Haar Cascade Classifier. Finally displayed text will be converted into speech using speech synthesizer.
Deep Learning with Nonparametric Clustering

Clustering is an essential problem in machine learning and data mining. One vital factor that impacts clustering performance is how to learn or design the data representation (or features). Fortunately, recent advances in deep learning can learn unsupervised features effectively, and have yielded state of the art performance in many classification problems, such as character recognition, object recognition and document categorization. However, little attention has been paid to the potential of deep learning for unsupervised clustering problems. In this paper, we propose a deep belief network with nonparametric clustering. As an unsupervised method, our model first leverages the advantages of deep learning for feature representation and dimension reduction. Then, it performs nonparametric clustering under a maximum margin framework – a discriminative clustering model and can be trained online efficiently in the code space. Lastly model parameters are refined in the deep belief network. Thus, this model can learn features for clustering and infer model complexity in an unified framework. The experimental results show the advantage of our approach over competitive baselines.
Pre-Trained Multi-View Word Embedding Using Two-Side Neural Network

Word embedding aims to learn a continuous representation for each word. It attracts increasing attention due to its effectiveness in various tasks such as named entity recognition and language modeling. Most existing word embedding results are generally trained on one individual data source such as news pages or Wikipedia articles. However, when we apply them to other tasks such as web search, the performance suffers. To obtain a robust word embedding for different applications, multiple data sources could be leveraged. In this paper, we proposed a two-side multimodal neural network to learn a robust word embedding from multiple data sources including free text, user search queries and search click-through data. This framework takes the word embeddings learned from different data sources as pre-train, and then uses a two-side neural network to unify these embeddings. The pre-trained embeddings are obtained by adapting the recently proposed CBOW algorithm. Since the proposed neural network does not need to re-train word embeddings for a new task, it is highly scalable in real world problem solving. Besides, the network allows weighting different sources differently when applied to different application tasks. Experiments on two real-world applications including web search ranking and word similarity measuring show that our neural network with multiple sources outperforms state-of-the-art word embedding algorithm with each individual source. It also outperforms other competitive baselines using multiple sources.
A 0.4-mW/Gb/s Near-Ground Receiver Front-End With Replica Transconductance Termination Calibration for a 16-Gb/s Source-Series Terminated Transceiver

This paper describes a low-power receiver front-end in a bidirectional near-ground source-series terminated (SST) interface implemented in a 40-nm CMOS process, which supports low-common mode differential NRZ signaling up to 16-Gb/s data rates. The high-speed operation is enabled by utilizing a common-gate amplifier stage with replica transconductance impedance calibration that accurately terminates the channel in the presence of receiver input loading. The near-ground low-impedance receiver also incorporates common-mode gain cancellation and in-situ equalization calibration to achieve reliable data reception at 16 Gb/s with better than 0.4 mW/Gb/s power efficiency over a memory link with more than 15 dB loss at the Nyquist frequency.
A 110-nm CMOS 0.7-V Input Transient-Enhanced Digital Low-Dropout Regulator With 99.98% Current Efficiency at 80-mA Load

This paper presents a digital low-dropout regulator (D-LDO) with a proposed transient-response boost technique, which enables the reduction of transient response time, as well as overshoot/undershoot, when the load current is abruptly drawn. The proposed D-LDO detects the deviation of the output voltage by overshoot/undershoot, and increases its loop gain, for the time that the deviation is beyond a limit. Once the output voltage is settled again, the loop gain is returned. With the D-LDO fabricated on an 110-nm CMOS technology, we measured its settling time and peak of undershoot, which were reduced by 60% and 72%, respectively, compared with and without the transient-response boost mode. Using the digital logic gates, the chip occupies a small area of 0.04 mm2, and it achieves a maximum current efficiency of 99.98%, by consuming the quiescent current of 15 μA at 0.7-V input voltage.
Automatic OpenCL work-group size selection for multicore CPUs

In this paper, we address the effect of the work-group size on the performance of OpenCL kernels. We propose a profiling-based algorithm that finds a good work-group size, in terms of performance, for the target multicore CPU architecture. Our algorithm reduces misses in the private L1 data cache and achieves load balancing between cores. It exploits the polyhedral model to estimate the working-set size and the number of cache misses for a parameterized work-group size of the OpenCL kernel. Based on the profiling information, it heuristically searches the space of parameterized work-group sizes. Our virtually-extended index space helps to increase the probability to find a better work-group size. We implement our work-group size selection algorithm as a development tool that consists of a code generator and a search library. The code generator extracts the polytope of each memory reference from the kernel code and generates a function that simplifies polytopes using the run-time information and invokes search library routines. The search library calculates the working-set size using the polytopes and finds a proper work-group size. We evaluate our approach using 31 OpenCL kernels on four different multicore CPUs. We compare its accuracy and search time to those of an exhaustive search method. Experimental results show that our tool is, on average, 1566 times faster than the exhaustive search and selects a work-group size whose performance is the same as or comparable to that of the exhaustive search.