nils-herrmann/clean_scirepeval huggingface dataset:
A Weighted Topic Model Enhanced Approach for complementary Collaborator Recommendation

Collaborations among interdisciplinary scientists are playing an increasingly important role in science innovations. As it is very difficult for a researcher to master the full knowledge of his/her targeted research areas, how to find suitable collaborators of complementary expertise has turned to be a key factor for researchers to succeed. With the expansion of the Web, the availability of sheer volume of information has resulted in information overload issue and posed significant challenges on determining appropriate scientists to collaborate with effectively for research opportunities. However, current studies on collaborator recommendation ignored this phenomenon and particularly overlooked the complementarity of their expertise within a restrictive context, i.e. for a given funding proposal or a research manuscript draft. In this study we propose a complementary expertise analysis enhanced approach to retrieval experts for research collaboration. It produces recommendation list using a heuristic greedy algorithm based on probabilistic topic model, and generates experts who ought to be complemented in expertise as well as to have good ability. The proposed method has been implemented in ScholarMate research community (www.scholarmate.com). We have conducted a user study to verify the effectiveness of the proposed approach and the preliminary results show its good performance comparing to the benchmarks.
Deep Cosine Metric Learning for Person Re-identification

Metric learning aims to construct an embedding where two extracted features corresponding to the same identity are likely to be closer than features from different identities. This paper presents a method for learning such a feature space where the cosine similarity is effectively optimized through a simple re-parametrization of the conventional softmax classification regime. At test time, the final classification layer can be stripped from the network to facilitate nearest neighbor queries on unseen individuals using the cosine similarity metric. This approach presents a simple alternative to direct metric learning objectives such as siamese networks that have required sophisticated pair or triplet sampling strategies in the past. The method is evaluated on two large-scale pedestrian re-identification datasets where competitive results are achieved overall. In particular, we achieve better generalization on the test set compared to a network trained with triplet loss.
phishGILLNET — phishing detection methodology using probabilistic latent semantic analysis , AdaBoost , and co-training

Identity theft is one of the most profitable crimes committed by felons. In the cyber space, this is commonly achieved using phishing. We propose here robust server side methodology to detect phishing attacks, called phishGILLNET, which incorporates the power of natural language processing and machine learning techniques. phishGILLNET is a multi-layered approach to detect phishing attacks. The first layer (phishGILLNET1) employs Probabilistic Latent Semantic Analysis (PLSA) to build a topic model. The topic model handles synonym (multiple words with similar meaning), polysemy (words with multiple meanings), and other linguistic variations found in phishing. Intentional misspelled words found in phishing are handled using Levenshtein editing and Google APIs for correction. Based on term document frequency matrix as input PLSA finds phishing and non-phishing topics using tempered expectation maximization. The performance of phishGILLNET1 is evaluated using PLSA fold in technique and the classification is achieved using Fisher similarity. The second layer of phishGILLNET (phishGILLNET2) employs AdaBoost to build a robust classifier. Using probability distributions of the best PLSA topics as features the classifier is built using AdaBoost. The third layer (phishGILLNET3) further expands phishGILLNET2 by building a classifier from labeled and unlabeled examples by employing Co-Training. Experiments were conducted using one of the largest public corpus of email data containing 400,000 emails. Results show that phishGILLNET3 outperforms state of the art phishing detection methods and achieves F-measure of 100%. Moreover, phishGILLNET3 requires only a small percentage (10%) of data be annotated thus saving significant time, labor, and avoiding errors incurred in human annotation.
Mobile Phone Use and Sleep Quality and Length in College Students

Proper sleep length and quality are essential for physical and mental health and have found to be related to a variety of negative outcomes (Brown, Buboltz, &Soper, 2002). College is a time,a transition for individuals where they begin laying a foundation for their future and acquiring sufficient sleep is of great value. College students are recognized as one of the most sleep-deprived groups, but also, as one of the most technologically-oriented population. Due to this combination, college students’ sleep habits and mobile phone use habits havebegun to receiveattention. The purpose of this study was to examine the relationship between sleep quality/length and mobile phone use among college students. Three hundred and fifty college students voluntarily participated by completing the Sleep Quality Index (SQI), The Sleep Habits Survey,the Mobile Phone Problem Use Scale(MPPUS), the SMS Problem Use Scale (SMS-PUDQ) and the Mini IPIP. Results indicate that various aspects of mobile phone use such as problem mobile phone use, addictive text messaging, problematic texting, and pathological texting are related to sleep quality, but not sleep length. Additionally, extraverted individuals were found to engage in greater mobile phone use and problematic text messaging.
Laplacian-guided image decolorization

In this paper we introduce a novel decolorization strategy built on image fusion principles. Decolorization (color-to-grayscale), is an important transformation used in many monochrome image processing applications. We demonstrate that aside from color spatial distribution, local information plays an important role in maintaining the discriminability of the image conversion. Our strategy blends the three color channels R, G, B guided by two weight maps that filter the local transitions and measure the dominant values of the regions using the Laplacian information. In order to minimize artifacts introduced by the weight maps, our fusion approach is designed in a multi-scale fashion, using a Laplacian pyramid decomposition. Additionally, compared with most of the existing techniques our straightforward technique has the advantage to be computationally effective. We demonstrate that our technique is temporal coherent being suitable to decolorize videos. A comprehensive qualitative and also quantitative evaluation based on an objective visual descriptor demonstrates the utility of our decolorization technique.
Designing for Labour: Uber and the On-Demand Mobile Workforce

Apps allowing passengers to hail and pay for taxi service on their phone? such as Uber and Lyft-have affected the livelihood of thousands of workers worldwide. In this paper we draw on interviews with traditional taxi drivers, rideshare drivers and passengers in London and San Francisco to understand how "ride-sharing" transforms the taxi business. With Uber, the app not only manages the allocation of work, but is directly involved in "labour issues": changing the labour conditions of the work itself. We document how Uber driving demands new skills such as emotional labour, while increasing worker flexibility. We discuss how the design of new technology is also about creating new labour opportunities -- jobs -- and how we might think about our responsibilities in designing these labour relations.
Deep Person Detection in 2D Range Data

Detecting humans is a key skill for mobile robots and intelligent vehicles in a large variety of applications. While the problem is well studied for certain sensory modalities such as image data, few works exist that address this detection task using 2D range data. However, a widespread sensory setup for many mobile robots in service and domestic applications contains a horizontally mounted 2D laser scanner. Detecting people from 2D range data is challenging due to the speed and dynamics of human leg motion and the high levels of occlusion and self-occlusion particularly in crowds of people. While previous approaches mostly relied on handcrafted features, we recently developed the deep learning based wheelchair and walker detector DROW. In this paper, we show the generalization to people, including small modifications that significantly boost DROW’s performance. Additionally, by providing a small, fully online temporal window in our network, we further boost our score. We extend the DROW dataset with person annotations, making this the largest dataset of person annotations in 2D range data, recorded during several days in a real-world environment with high diversity. Extensive experiments with three current baseline methods indicate it is a challenging dataset, on which our improved DROW detector beats the current state-of-the-art.
Research trends in automotive functional safety

In recent years, most of the modern automobiles are equipped with embedded electronic systems which include lots of Electronic Controller Units (ECUs), electronic sensors, signals, bus systems and coding. Due to the complex application in electrical, electronics and programmable electronics, it is necessary to analyze the potential risk of malfunction for automotive systems. IEC 61508 has become a foundation for international standard of safety-related system such as airborne systems, railway, nuclear power plants, medical equipment, energy and process systems, machinery, furnaces and automobiles. The failure of such system could have significant impact on the safety of humans and/or the environment. Thus, ISO 26262 has been introduced in November, 2011 for automotive electrical/electronic (E/E) systems which emphasizing the complete safety installation from sensor to actuator with its technical as well as management issues. In this paper, the international trends pre and post introduction of ISO 26262 is analyzed, and the potential development direction in this area is discussed.
A General Optimization Framework for Multi-Document Summarization Using Genetic Algorithms and Swarm Intelligence

Extracting summaries via integer linear programming and submodularity are popular and successful techniques in extractive multi-document summarization. However, many interesting optimization objectives are neither submodular nor factorizable into an integer linear program. We address this issue and present a general optimization framework where any function of input documents and a system summary can be plugged in. Our framework includes two kinds of summarizers – one based on genetic algorithms, the other using a swarm intelligence approach. In our experimental evaluation, we investigate the optimization of two information-theoretic summary evaluation metrics and find that our framework yields competitive results compared to several strong summarization baselines. Our comparative analysis of the genetic and swarm summarizers reveals interesting complementary properties.
Introduction to React

React teaches you React, the JavaScript framework created by developers at Facebook, to solve the problem of building complex user interfaces in a consistent and maintainable way. React.js shrugs away common front-end conventions in an effort to make things more effi cient use Introduction to React to learn about this framework and more today. Get to know the React API and it’s specifi c JavaScript extension, JSX, which makes authoring React components easier and maintainable. You will also learn how to test your React applications and about the tools you can use while building. Once you understand these core concepts, you can build applications with React. This will help you cement the ideas and fundamentals of React and prepare you to utilize React in your own use case.
Electrically Large Zero-Phase-Shift Line Grid-Array UHF Near-Field RFID Reader Antenna

A grid-array antenna using a zero-phase-shift (ZPS) line is proposed to enlarge the interrogation zone of a reader antenna for near-field ultra-high-frequency (UHF) radio frequency identification (RFID) system. The proposed grid-array antenna is composed of a number of grid cells and a double-sided parallel-strip line feeding network. Each grid cell, namely segmented loop constructed by the ZPS line, has uniform and single-direction flowing current along the line. By configuration of the cells sharing a common side with its adjacent grid cells which carry reverse-direction flowing current, a grid-array antenna is formed to generate a strong and uniform magnetic-field distribution over a large interrogation zone even when the perimeter of the interrogation zone reaches up to 3λ (where λ is the operating wavelength in free space) or larger. As an example, a grid-array antenna with 1 × 2 segmented ZPS line loop cells implemented onto a piece of FR4 printed board (PCB) is designed and prototyped. The results show that the grid-array antenna achieves the impedance matching over the frequency range from 790 to 1040 MHz and produces strong and uniform magnetic-field distribution over an interrogation zone of 308 mm × 150 mm.
3-D Deep Learning Approach for Remote Sensing Image Classification

Recently, a variety of approaches have been enriching the field of remote sensing (RS) image processing and analysis. Unfortunately, existing methods remain limited to the rich spatiospectral content of today’s large data sets. It would seem intriguing to resort to deep learning (DL)-based approaches at this stage with regard to their ability to offer accurate semantic interpretation of the data. However, the specificity introduced by the coexistence of spectral and spatial content in the RS data sets widens the scope of the challenges presented to adapt DL methods to these contexts. Therefore, the aim of this paper is first to explore the performance of DL architectures for the RS hyperspectral data set classification and second to introduce a new 3-D DL approach that enables a joint spectral and spatial information process. A set of 3-D schemes is proposed and evaluated. Experimental results based on well-known hyperspectral data sets demonstrate that the proposed method is able to achieve a better classification rate than state-of-the-art methods with lower computational costs.
A Systematic Literature Review on Decomposition Approaches to Estimate Time Series Components

The study and modeling of systems have called the attention of several researchers, who are interested in estimating rules to describe data behavior. However, before proceeding with this estimation, it is necessary to understand the intrinsic features embedded in data. When such features are not correctly analyzed, the model accuracy tends to decrease. A well-known way to perform this analysis is by the study of time series behavior according to their stochastic and deterministic components. Nevertheless, the time series decomposition into these components is not a simple task. In order to address this issue, we conducted a rigorous and well-structured search for scientific papers in different repositories. By analyzing the recovered papers, we drew relevant conclusions such as: which methods are commonly used to decompose time series; the frequency of published papers per year; and the gaps of each method. Moreover, we have also classified the most suitable studies to estimate the determinism and stochasticity present in time series. After conducting this study, we concluded the development of methods to decompose time series into stochastic and deterministic components is still an open problem.
Ultrasonic Fingerprint Sensor With Transmit Beamforming Based on a PMUT Array Bonded to CMOS Circuitry

In this paper, we present a single-chip  $65 \times 42$  element ultrasonic pulse-echo fingerprint sensor with transmit (TX) beamforming based on piezoelectric micromachined ultrasonic transducers directly bonded to a CMOS readout application-specific integrated circuit (ASIC). The readout ASIC was realized in a standard 180-nm CMOS process with a 24-V high-voltage transistor option. Pulse-echo measurements are performed column-by-column in sequence using either one column or five columns to TX the ultrasonic pulse at 20 MHz. TX beamforming is used to focus the ultrasonic beam at the imaging plane where the finger is located, increasing the ultrasonic pressure and narrowing the 3-dB beamwidth to  $50~\mu \text{m}$ , a factor of 6.4 narrower than nonbeamformed measurements. The surface of the sensor is coated with a poly-dimethylsiloxane (PDMS) layer to provide good acoustic impedance matching to skin. Scanning laser Doppler vibrometry of the PDMS surface was used to map the ultrasonic pressure field at the imaging surface, demonstrating the expected increase in pressure, and reduction in beamwidth. Imaging experiments were conducted using both PDMS phantoms and real fingerprints. The average image contrast is increased by a factor of 1.5 when beamforming is used.
Event-Based Moving Object Detection and Tracking

Event-based vision sensors, such as the Dynamic Vision Sensor (DVS), are ideally suited for real-time motion analysis. The unique properties encompassed in the readings of such sensors provide high temporal resolution, superior sensitivity to light and low latency. These properties provide the grounds to estimate motion efficiently and reliably in the most sophisticated scenarios, but these advantages come at a price - modern event-based vision sensors have extremely low resolution, produce a lot of noise and require the development of novel algorithms to handle the asynchronous event stream. This paper presents a new, efficient approach to object tracking with asynchronous cameras. We present a novel event stream representation which enables us to utilize information about the dynamic (temporal)component of the event stream. The 3D geometry of the event stream is approximated with a parametric model to motion-compensate for the camera (without feature tracking or explicit optical flow computation), and then moving objects that don't conform to the model are detected in an iterative process. We demonstrate our framework on the task of independent motion detection and tracking, where we use the temporal model inconsistencies to locate differently moving objects in challenging situations of very fast motion.
Manufacturing tolerances for UV LIGA using SU-8 resist

UV LIGA involves the exposure of SU-8 negative resist, using a UV mask aligner, to produce high aspect ratio pillars or microchannels as part of the manufacturing process for microsystems. This has been made possible by the widespread use of a UV sensitive resist SU-8. Many papers have been written on the Fresnel diffraction theory of exposure, some key properties of SU-8 and prototype applications, but little attention has been paid to the variation in parameters that affect the tolerances and hence the reproducibility with which component dimensions can be maintained. It will be shown that the accuracy and precision, with which a feature on a mask can be reproduced throughout a thick resist structure, will depend on key parameters in the set-up of the mask aligner, the material properties of the SU-8 resist and the thickness of the resist structure. A simplified optical exposure model is used to predict the dimensions and wall taper angles of resist pillars or microchannels and their variation with changes in these parameters.
Vishit: A Visualizer for Hindi Text

We outline the design of a visualizer, named Vishit, for texts in the Hindi language. The Hindi language is lingua franca in many states of India where people speak different languages. The visualized text serves as a universal language where seamless communication is needed by many people who speak different languages and have different cultures. Vishit consists of the following three major processing steps: language processing, knowledge base creation and scene generation. Initial results from the Vishit prototype are encouraging.
Deep convolutional neural network for latent fingerprint enhancement

In this work, we propose a novel latent fingerprint enhancement method based on FingerNet inspired by recent development of Convolutional Neural Network (CNN). Although CNN is achieving superior performance in many computer vision tasks from low-level image processing to high-level semantic understanding, limited attention has been paid in fingerprint community. The proposed FingerNet has three major parts: one common convolution part shared by two different deconvolution parts, which are the enhancement branch and the orientation branch. The convolution part is to extract fingerprint features particularly for enhancement purpose. The enhancement deconvolution branch is employed to remove structured noise and enhance fingerprints as its task. The orientation deconvolution branch performs the task of guiding enhancement through a multi-task learning strategy. The network is trained in the manner of pixels-to-pixels and end-to-end learning, that can directly enhance latent fingerprint as the output. We also study some implementation details such as single-task learning, multi-task learning, and the residual learning. Experimental results of the FingerNet system on latent fingerprint dataset NIST SD27 demonstrate effectiveness and robustness of the proposed method.
A fad or future of IT?: A comprehensive literature review on the cloud computing research

Cloud computing is now a global trend and during the past decade, has drawn attention from both academic and business communities. Although the evolution of cloud computing has not reached the maturity level, there is still adequate research about the topic. The main purpose of this paper is to examine the development and evolution of cloud computing over time. A content analysis was conducted for 236 scholarly journal articles, which were published between 2009 and 2014 in order to (i) identify the possible trends and changes in cloud computing over the six years, (ii) compare publishing productivity of journals about the cloud computing subject, and (iii) guide future research about cloud computing. The results show that the majority of the cloud computing research is about “cloud computing adoption” (19%), and it was followed by the “legal and ethical issues” of cloud computing (15%). It is also found that “cloud computing for mobile applications” (6%), “benefits & challenges of cloud computing” (5%) and “energy consumption dimension of cloud computing” (4%) are the least attention-grabbing themes in the literature. However, “cloud computing for mobile applications” and “energy consumption dimension of cloud computing” themes have become popular in the last two years, so they are expected to be trendy topics of the near future. Finally, another finding of this study is that the majority of the articles were published by engineering, information systems or technical journals such as “IT Professional Magazine”, “International Journal of Information Management” and “Mobile Networks and Applications”. It seems as if this topic is generally ignored by the managerial and organizational journals even though the impact of cloud computing on organizations and institutions is immense and is in need of investigation.