nils-herrmann/clean_scirepeval huggingface dataset:
Job Performance

Individual performance is of high relevance for organizations and individuals alike. Showing high performance when accomplishing tasks results in satisfaction, feelings of selfefficacy and mastery (Bandura, 1997; Kanfer et aL, 2005). Moreover, high performing individuals get promoted, awarded and honored. Career opportunities for individuals who perform well are much better than those of moderate or low performing individuals (Van Scotter et aI., 2000). This chapter summarizes research on individual performance and addresses performance as a multi-dimensional and dynamic concept. First, we define the concept of performance, next we discuss antecedents of between-individual variation of performance, and describe intraindividual change and variability in performance, and finally, we present a research agenda for future research.
A High-Efficiency 24 GHz Rectenna Development Towards Millimeter-Wave Energy Harvesting and Wireless Power Transmission

This work addresses design and implementation issues of a 24 GHz rectenna, which is developed to demonstrate the feasibility of wireless power harvesting and transmission (WPT) techniques towards millimeter-wave regime. The proposed structure includes a compact circularly polarized substrate integrated waveguide (SIW) cavity-backed antenna array integrated with a self-biased rectifier using commercial Schottky diodes. The antenna and the rectifier are individually designed, optimized, fabricated and measured. Then they are integrated into one circuit in order to validate the studied rectenna architecture. The maximum measured conversion efficiency and DC voltage are respectively equal to 24% and 0.6 V for an input power density of 10 mW/cm2.
Assessment of Breast Cancer Histology using Densely Connected Convolutional Networks

Breast cancer is the most frequently diagnosed cancer and leading cause of cancer-related death among females worldwide. In this article, we investigate the applicability of densely connected convolutional neural networks to the problems of histology image classification and whole slide image segmentation in the area of computer-aided diagnoses for breast cancer. To this end, we study various approaches for transfer learning and apply them to the data set from the 2018 grand challenge on breast cancer histology images (BACH).
A remote mobile collaborative AR system for learning in physics

This research demo describes the implementation of a mobile AR-supported educational course application, AR Circuit, which is designed to promote the effectiveness of remote collaborative learning for physics. The application employs the TCP/IP protocol enabling multiplayer functionality in a mobile AR environment. One phone acts as the server and the other acts as the client. The server phone will capture the video frames, process the video frame, and send the current frame and the markers transformation matrices to the client phone.
Token-based authentication for smartphones

Due to short - but frequent - sessions of smartphone usage, the fast and easy usability of authentication mechanisms in this special environment has a big impact on user acceptance. In this work we propose a user-friendly alternative to common authentication methods (like PINs and patterns). The advantages of the proposed method are its security, fastness, and easy usage, requiring minimal user interaction compared to other authentication techniques currently used on smartphones. The mechanism described uses the presence of a Bluetooth-connected hardware-token to authenticate the user and can easily be implemented on current smartphones. It is based on an authentication protocol which meets the requirements on energy efficiency and limited resources by optimizing the communication effort. A prototype was implemented on an Android smartphone and an MSP430 based MCU. The token allows fast authentication without the need for additional user action. The entire authentication process can be completed in less than one second, the developed software prototype requires no soft- or hardware modifications (like rooting) of the Android phone.
Euro Banknote Recognition System for Blind People

This paper presents the development of a portable system with the aim of allowing blind people to detect and recognize Euro banknotes. The developed device is based on a Raspberry Pi electronic instrument and a Raspberry Pi camera, Pi NoIR (No Infrared filter) dotted with additional infrared light, which is embedded into a pair of sunglasses that permit blind and visually impaired people to independently handle Euro banknotes, especially when receiving their cash back when shopping. The banknote detection is based on the modified Viola and Jones algorithms, while the banknote value recognition relies on the Speed Up Robust Features (SURF) technique. The accuracies of banknote detection and banknote value recognition are 84% and 97.5%, respectively.
Malicious Software Classification using VGG 16 Deep Neural Network ’ s Bottleneck Features

Malicious software (malware) has been extensively employed for illegal purposes and thousands of new samples are discovered every day. The ability to classify samples with similar characteristics into families makes possible to create mitigation strategies that work for a whole class of programs. In this paper, we present a malware family classification approach using VGG16 deep neural network’s bottleneck features. Malware samples are represented as byteplot grayscale images and the convolutional layers of a VGG16 deep neural network pre-trained on the ImageNet dataset is used for bottleneck features extraction. These features are used to train a SVM classifier for the malware family classification task. The experimental results on a dataset comprising 10,136 samples from 20 different families showed that our approach can effectively be used to classify malware families with an accuracy of 92.97%, outperforming similar approaches proposed in the literature which require feature engineering and considerable domain expertise.
Lattice Long Short-Term Memory for Human Action Recognition

Human actions captured in video sequences are threedimensional signals characterizing visual appearance and motion dynamics. To learn action patterns, existing methods adopt Convolutional and/or Recurrent Neural Networks (CNNs and RNNs). CNN based methods are effective in learning spatial appearances, but are limited in modeling long-term motion dynamics. RNNs, especially Long Short- Term Memory (LSTM), are able to learn temporal motion dynamics. However, naively applying RNNs to video sequences in a convolutional manner implicitly assumes that motions in videos are stationary across different spatial locations. This assumption is valid for short-term motions but invalid when the duration of the motion is long.,,In this work, we propose Lattice-LSTM (L2STM), which extends LSTM by learning independent hidden state transitions of memory cells for individual spatial locations. This method effectively enhances the ability to model dynamics across time and addresses the non-stationary issue of long-term motion dynamics without significantly increasing the model complexity. Additionally, we introduce a novel multi-modal training procedure for training our network. Unlike traditional two-stream architectures which use RGB and optical flow information as input, our two-stream model leverages both modalities to jointly train both input gates and both forget gates in the network rather than treating the two streams as separate entities with no information about the other. We apply this end-to-end system to benchmark datasets (UCF-101 and HMDB-51) of human action recognition. Experiments show that on both datasets, our proposed method outperforms all existing ones that are based on LSTM and/or CNNs of similar model complexities.
Factors Influencing Consumer Adoption of Internet Banking in India

The Banking industry has undergone a dramatic change since internet penetration and the concept of internet banking. Internet banking is defined as an internet portal, through which customers can use different kinds of banking services. Internet banking has major effects on banking relationships. The primary objective of this research is to identify the factors that influence internet banking adoption. Using PLS, a model is successfully proved and it is found that internet banking is influenced by its perceived reliability, Perceived ease of use and Perceived usefulness. In the marketing process of internet banking services marketing experts should emphasize these benefits its adoption provides and awareness can also be improved to attract consumers’ attention to internet banking services. Factors Influencing Consumer Adoption of Internet Banking in India 1 Assistant professor, Karunya School of Management, Karunya University, Coimbatore, India.
A Deep Q-Network for the Beer Game: A Deep Reinforcement Learning algorithm to Solve Inventory Optimization Problems

The beer game is a widely used in-class game that is played in supply chain management classes to demonstrate the bullwhip effect. The game is a decentralized, multi-agent, cooperative problem that can be modeled as a serial supply chain network in which agents cooperatively attempt to minimize the total cost of the network even though each agent can only observe its own local information. Each agent chooses order quantities to replenish its stock. Under some conditions, a base-stock replenishment policy is known to be optimal. However, in a decentralized supply chain in which some agents (stages) may act irrationally (as they do in the beer game), there is no known optimal policy for an agent wishing to act optimally. We propose a machine learning algorithm, based on deep Q-networks, to optimize the replenishment decisions at a given stage. When playing alongside agents who follow a base-stock policy, our algorithm obtains near-optimal order quantities. It performs much better than a base-stock policy when the other agents use a more realistic model of human ordering behavior. Unlike most other algorithms in the literature, our algorithm does not have any limits on the beer game parameter values. Like any deep learning algorithm, training the algorithm can be computationally intensive, but this can be performed ahead of time; the algorithm executes in real time when the game is played. Moreover, we propose a transfer learning approach so that the training performed for one agent and one set of cost coefficients can be adapted quickly for other agents and costs. Our algorithm can be extended to other decentralized multi-agent cooperative games with partially observed information, which is a common type of situation in real-world supply chain problems.
Recent advances in document summarization

The task of automatic document summarization aims at generating short summaries for originally long documents. A good summary should cover the most important information of the original document or a cluster of documents, while being coherent, non-redundant and grammatically readable. Numerous approaches for automatic summarization have been developed to date. In this paper we give a self-contained, broad overview of recent progress made for document summarization within the last 5 years. Specifically, we emphasize on significant contributions made in recent years that represent the state-of-the-art of document summarization, including progress on modern sentence extraction approaches that improve concept coverage, information diversity and content coherence, as well as attempts from summarization frameworks that integrate sentence compression, and more abstractive systems that are able to produce completely new sentences. In addition, we review progress made for document summarization in domains, genres and applications that are different from traditional settings. We also point out some of the latest trends and highlight a few possible future directions.
3 D-PRNN : Generating Shape Primitives with Recurrent Neural Networks

The success of various applications including robotics, digital content creation, and visualization demand a structured and abstract representation of the 3D world from limited sensor data. Inspired by the nature of human perception of 3D shapes as a collection of simple parts, we explore such an abstract shape representation based on primitives. Given a single depth image of an object, we present 3DPRNN, a generative recurrent neural network that synthesizes multiple plausible shapes composed of a set of primitives. Our generative model encodes symmetry characteristics of common man-made objects, preserves long-range structural coherence, and describes objects of varying complexity with a compact representation. We also propose a method based on Gaussian Fields to generate a large scale dataset of primitive-based shape representations to train our network. We evaluate our approach on a wide range of examples and show that it outperforms nearest-neighbor based shape retrieval methods and is on-par with voxelbased generative models while using a significantly reduced parameter space.
Introduction to Time-Sensitive Networking

A number of companies and standards development organizations have, since 2000, been producing products and standards for "time-sensitive networks" to support real-time applications that require a) zero packet loss due to buffer congestion, b) extremely low packet loss due to equipment failure, and c) guaranteed upper bounds on end-to-end latency. Often, a robust capability for time synchronization to less than 1 μs is also required. These networks consist of specially-featured bridges that are interconnected using standard Ethernet links with standard MAC/PHY layers. Since 2012, this technology has advanced to the use of routers, as well as bridges, and features of interest to time-sensitive networks have been added to both Ethernet and wireless standards.
Ensemble Learning for Named Entity Recognition

A considerable portion of the information on the Web is still only available in unstructured form. Implementing the vision of the Semantic Web thus requires transforming this unstructured data into structured data. One key step during this process is the recognition of named entities. Previous works suggest that ensemble learning can be used to improve the performance of named entity recognition tools. However, no comparison of the performance of existing supervised machine learning approaches on this task has been presented so far. We address this research gap by presenting a thorough evaluation of named entity recognition based on ensemble learning. To this end, we combine four different state-of-the approaches by using 15 different algorithms for ensemble learning and evaluate their performace on five different datasets. Our results suggest that ensemble learning can reduce the error rate of state-of-the-art named entity recognition systems by 40%, thereby leading to over 95% f-score in our best run.
Crowdfunding genomics and bioinformatics

Online crowdfunding websites such as RocketHub, Indiegogo and Kickstarter have financed an increasingly eclectic variety of initiatives: multimillion dollar movie projects attached to big Hollywood names, music and book publishing, gadget development, a hoodie that lasts ten years. Controversially, one crowdfunding campaign aimed to raise funds for a drug dealer, who it was thought might be persuaded to hand over compromising video footage of a prominent politician in exchange for the cash. Recently, a number of scientists have sought to use crowdfunding as a means to bypass traditional funding routes when budgeting for new projects. In doing so, they are expanding on the concept of citizen science from crowd participation to crowdfunding. As is the traditional crowdfunding method, these scientists hope to attract funding using two main incentives: (1) the desire by the funder to see the project get off the ground, and (2) the acknowledgement of donations with a range of rewards – some of significant monetary value. Donors also have the security of knowing that payment will only be taken once a project is fully funded, thereby reducing the risk of wasting money on a dud. To start crowdfunding your own science, or to discover projects to donate to, you can now even use a dedicated crowdfunding website for scientific research, Microryza (https://www.microryza.com/), alongside the more general crowdfunding websites. Rather than receive physical rewards, donors on Microryza gain exclusive access to updates on the progress of their funded research. Here, three sets of scientists describe their experience of crowdfunding projects in the fields of genomics and bioinformatics: PathoMap (http://www.indiegogo.com/ projects/pathomap-mapping-nyc-s-microscopic-residents),
Subsampled Exponential Mechanism: Differential Privacy in Large Output Spaces

In the last several years, differential privacy has become the leading framework for private data analysis. It provides bounds on the amount that a randomized function can change as the result of a modification to one record of a database. This requirement can be satisfied by using the exponential mechanism to perform a weighted choice among the possible alternatives, with better options receiving higher weights. However, in some situations the number of possible outcomes is too large to compute all weights efficiently. We present the subsampled exponential mechanism, which scores only a sample of the outcomes. We show that it still preserves differential privacy, and fulfills a similar accuracy bound. Using a clustering application, we show that the subsampled exponential mechanism outperforms a previously published private algorithm and is comparable to the full exponential mechanism but more scalable.
Secure Pub-Sub: Blockchain-Based Fair Payment With Reputation for Reliable Cyber Physical Systems

The cyber physical system (CPS) has gained considerable success in large-scale distributed integration environment. In such systems, the sensor devices collect data which would be disseminated via reliable manner to all interested co-operant entities from the physical world. However, highly unreliable environment of CPS, for example, a number of limitations of existing network middle wares, makes secure and reliable data distribution services a challenge issue. In this paper, we propose a new architecture called secure pub-sub (SPS) without middle ware, i.e., blockchain-based fair payment with reputation. In SPS, publishers publish a topic on the blockchain and subscribers specify an interest message by making a deposit to subscribing the topic. Then, if the interest message matches the topic, the publisher transmits the encrypted content of the topic to the blockchain such that the subscribers can decrypt the ciphertext to obtain the content, and mark the publisher as its reputation. Finally, the publisher receives the payment from the subscriber. The new proposal provides confidentiality and reliability of data, anonymity of subscribers and payment fairness between the publishers and subscribers. Different from the traditional pub-sub services, no trusted third party is involved in our system due to employing blockchain technique. The security of the proposed SPS is analyzed as well. The implementation of the protocol on Ethereum of smart contract demonstrates the validity of SPS.
Anticipating Economic Market Crises Using Measures of Collective Panic

Predicting panic is of critical importance in many areas of human and animal behavior, notably in the context of economics. The recent financial crisis is a case in point. Panic may be due to a specific external threat or self-generated nervousness. Here we show that the recent economic crisis and earlier large single-day panics were preceded by extended periods of high levels of market mimicry--direct evidence of uncertainty and nervousness, and of the comparatively weak influence of external news. High levels of mimicry can be a quite general indicator of the potential for self-organized crises.
Mindfulness for Long-Distance Runners : An Open Trial Using Mindful Sport Performance Enhancement ( MSPE )

The present study sought to determine the effects of Mindful Sport Performance Enhancement (MSPE) on runners. Participants were 25 recreational long-distance runners openly assigned to either the 4-week intervention or to a waiting-list control group, which later received the same program. Results indicate that the MSPE group showed significantly more improvement in organizational demands (an aspect of perfectionism) compared with controls. Analyses of preto postworkshop change found a significant increase in state mindfulness and trait awareness and decreases in sport-related worries, personal standards perfectionism, and parental criticism. No improvements in actual running performance were found. Regression analyses revealed that higher ratings of expectations and credibility of the workshop were associated with lower postworkshop perfectionism, more years running predicted higher ratings of perfectionism, and more life stressors predicted lower levels of worry. Findings suggest that MSPE may be a useful mental training intervention for improving mindfulness, sport-anxiety related worry, and aspects of perfectionism in long-distance runners.
Do Topic Shift and Query Reformulation Patterns Correlate in Academic Search?

While it is known that academic searchers differ from typical web searchers, little is known about the search behavior of academic searchers over longer periods of time. In this study we take a look at academic searchers through a large-scale log analysis on a major academic search engine. We focus on two aspects: query reformulation patterns and topic shifts in queries. We first analyze how each of these aspects evolve over time. We identify important query reformulation patterns: revisiting and issuing new queries tend to happen more often over time. We also find that there are two distinct types of users: one type of users becomes increasingly focused on the topics they search for as time goes by, and the other becomes increasingly diversifying. After analyzing these two aspects separately, we investigate whether, and to which degree, there is a correlation between topic shifts and query reformulations. Surprisingly, users’ preferences of query reformulations correlate little with their topic shift tendency. However, certain reformulations may help predict the magnitude of the topic shift that happens in the immediate next timespan. Our results shed light on academic searchers’ information seeking behavior and may benefit search personalization.
Multimedia Big Data Analytics: A Survey

With the proliferation of online services and mobile technologies, the world has stepped into a multimedia big data era. A vast amount of research work has been done in the multimedia area, targeting different aspects of big data analytics, such as the capture, storage, indexing, mining, and retrieval of multimedia big data. However, very few research work provides a complete survey of the whole pine-line of the multimedia big data analytics, including the management and analysis of the large amount of data, the challenges and opportunities, and the promising research directions. To serve this purpose, we present this survey, which conducts a comprehensive overview of the state-of-the-art research work on multimedia big data analytics. It also aims to bridge the gap between multimedia challenges and big data solutions by providing the current big data frameworks, their applications in multimedia analyses, the strengths and limitations of the existing methods, and the potential future directions in multimedia big data analytics. To the best of our knowledge, this is the first survey that targets the most recent multimedia management techniques for very large-scale data and also provides the research studies and technologies advancing the multimedia analyses in this big data era.