nils-herrmann/clean_scirepeval huggingface dataset:
A dark side of the American dream: correlates of financial success as a central life aspiration.

Aspiring for financial success is an important aspect of capitalist cultures. Three studies examine the hypothesis that values and expectancies for wealth and money are negatively associated with adjustment and well-being when they are more central to an individual than other self-relevant values and expectancies. Studies 1 and 2 use 2 methods to show that the relative centrality of money-related values and expectancies is negatively related to college students' well-being and mental health. Study 3, using a heterogeneous noncollege sample, extends these findings by showing that a high centrality of aspirations for financial success is associated with interview ratings of lower global adjustment and social productivity and more behavioral disorders. Discussion is focused on the deleterious consequences of materialistic world views and the need to examine differential effects of content regarding goals and values.
Improving robot navigation in structured outdoor environments by identifying vegetation from laser data

This paper addresses the problem of vegetation detection from laser measurements. The ability to detect vegetation is important for robots operating outdoors, since it enables a robot to navigate more efficiently and safely in such environments. In this paper, we propose a novel approach for detecting low, grass-like vegetation using laser remission values. In our algorithm, the laser remission is modeled as a function of distance, incidence angle, and material. We classify surface terrain based on 3D scans of the surroundings of the robot. The model is learned in a self-supervised way using vibration-based terrain classification. In all real world experiments we carried out, our approach yields a classification accuracy of over 99%. We furthermore illustrate how the learned classifier can improve the autonomous navigation capabilities of mobile robots.
Authenticated Key Exchange over Bitcoin

Bitcoin is designed to protect user anonymity (or pseudonymity) in a financial transaction, and has been increasingly adopted by major ecommerce websites such as Dell, PayPal and Expedia. While the anonymity of Bitcoin transactions has been extensively studied, little attention has been paid to the security of post-transaction correspondence. In a commercial application, the merchant and the user often need to engage in follow-up correspondence after a Bitcoin transaction is completed, e.g., to acknowledge the receipt of payment, to confirm the billing address, to arrange the product delivery, to discuss refund and so on. Currently, such follow-up correspondence is typically done in plaintext via email with no guarantee on confidentiality. Obviously, leakage of sensitive data from the correspondence (e.g., billing address) can trivially compromise the anonymity of Bitcoin users. In this paper, we initiate the first study on how to realise end-to-end secure communication between Bitcoin users in a post-transaction scenario without requiring any trusted third party or additional authentication credentials. This is an important new area that has not been covered by any IEEE or ISO/IEC security standard, as none of the existing PKI-based or password-based AKE schemes are suitable for the purpose. Instead, our idea is to leverage the Bitcoin’s append-only ledger as an additional layer of authentication between previously confirmed transactions. This naturally leads to a new category of AKE protocols that bootstrap trust entirely from the block chain. We call this new category “Bitcoin-based AKE” and present two concrete protocols: one is non-interactive with no forward secrecy, while the other is interactive with additional guarantee of forward secrecy. Finally, we present proof-of-concept prototypes for both protocols with experimental results to demonstrate their practical feasibility.
LATENT SEMANTIC INDEXING FOR PATENT DOCUMENTS

Since the huge amount of patent documents database is continuously increasing, the issue of classifying, updating and retrieving patent documents turned into an acute necessity. Therefore we investigate the efficiency of applying Latent Semantic Indexing, an automatic indexing method for information retrieval, to some classes of patent documents from the United States Patent Classification System. We present some experiments that provide the optimal number of dimensions for the Latent Semantic Space and we compare the performance of Latent Semantic Indexing to the Vector Space Model technique applied to real life text documents, namely patent documents.
Simulation Research of Exoskeleton Suit Based on Sensitivity Amplification Control

Exoskeleton suit is a kind of human-machine robot, which combines the humans intelligence with the powerful energy of mechanism. It can help people to carry heavy load, walking on kinds of terrains and have a broadly apply area. Though many exoskeleton suits has been developed, there need many complex sensors between the pilot and the exoskeleton system, which decrease the comfort of the pilot. Sensitivity amplification control (SAC) is a method applied in exoskeleton system without any sensors between the pilot and the exoskeleton. In this paper simulation research was made to verify the feasibility of SAC include a simple 1-dof model and a swing phase model of 3-dof. A PID controller was taken to describe the human-machine interface model. Simulation results show the human only need to exert a scale-down version torque compared with the actuator and decrease the power consumes of the pilot.
The Nonsubsampled Contourlet Transform: Theory, Design, and Applications

In this paper, we develop the nonsubsampled contourlet transform (NSCT) and study its applications. The construction proposed in this paper is based on a nonsubsampled pyramid structure and nonsubsampled directional filter banks. The result is a flexible multiscale, multidirection, and shift-invariant image decomposition that can be efficiently implemented via the a trous algorithm. At the core of the proposed scheme is the nonseparable two-channel nonsubsampled filter bank (NSFB). We exploit the less stringent design condition of the NSFB to design filters that lead to a NSCT with better frequency selectivity and regularity when compared to the contourlet transform. We propose a design framework based on the mapping approach, that allows for a fast implementation based on a lifting or ladder structure, and only uses one-dimensional filtering in some cases. In addition, our design ensures that the corresponding frame elements are regular, symmetric, and the frame is close to a tight one. We assess the performance of the NSCT in image denoising and enhancement applications. In both applications the NSCT compares favorably to other existing methods in the literature
Modifying MCTS for Human-Like General Video Game Playing

We address the problem of making general video game playing agents play in a human-like manner. To this end, we introduce several modifications of the UCT formula used in Monte Carlo Tree Search that biases action selection towards repeating the current action, making pauses, and limiting rapid switching between actions. Playtraces of human players are used to model their propensity for repeated actions; this model is used for biasing the UCT formula. Experiments show that our modified MCTS agent, called BoT, plays quantitatively similar to human players as measured by the distribution of repeated actions. A survey of human observers reveals that the agent exhibits human-like playing style in some games but not others.
An ensemble approach for multi-label classification of item click sequences

In this paper, we describe our approach to RecSys 2015 challenge problem. Given a dataset of item click sessions, the problem is to predict whether a session results in a purchase and which items are purchased if the answer is yes.
 We define a simpler analogous problem where given an item and its session, we try to predict the probability of purchase for the given item. For each session, the predictions result in a set of purchased items or often an empty set.
 We apply monthly time windows over the dataset. For each item in a session, we engineer features regarding the session, the item properties, and the time window. Then, a balanced random forest classifier is trained to perform predictions on the test set.
 The dataset is particularly challenging due to privacy-preserving definition of a session, the class imbalance problem, and the volume of data. We report our findings with respect to feature engineering, the choice of sampling schemes, and classifier ensembles. Experimental results together with benefits and shortcomings of the proposed approach are discussed. The solution is efficient and practical in commodity computers.
Digit Classification on Signboards for Telephone Number Recognition

This paper presents a digits classification system to recognize telephone numbers written on signboards. Candidate regions of digits are extracted from an image through edge extraction, enhancement and labeling. Since the digits in the images often have skew and slant, the digits are recognized after the skew and slant correction. To correct the skew, Hough transform is used, and the slant is corrected using the method of circumscribing digits with tilted rectangles. In experiments, we tested a total of 1,332 images of signboards with 11,939 digits. We obtained a digit extraction rate of 99.2% and a correct digit recognition rate of 98.8%.
Guidelines for evaluation of radio interface technologies for IMT-Advanced

The content herein was prepared by a group of technical experts in IEEE 802 and industry and was approved for submission by the IEEE 802.11TM Working Group on Wireless Local Area Networks, IEEE 802.16TM Working Group on Broadband Wireless Access, the IEEE 802.18 Radio Regulatory Technical Advisory Group, IEEE 802.20TM Working Group on Mobile Broadband Wireless Access, and the IEEE 802 Executive Committee, in accordance with the IEEE 802 policies and procedures, and represents the view of IEEE 802.
Cryptographic Processors-A Survey

Tamper-resistant cryptographic processors are becoming the standard way to enforce data-usage policies. Their origins lie with military cipher machines and PIN processing in banking payment networks, expanding in the 1990s into embedded applications: token vending machines for prepayment electricity and mobile phone credit. Major applications such as GSM mobile phone identification and pay TV set-top boxes have pushed low-cost cryptoprocessors toward ubiquity. In the last five years, dedicated crypto chips have been embedded in devices such as game console accessories and printer ink cartridges, to control product and accessory after markets. The "Trusted Computing" initiative will soon embed cryptoprocessors in PCs so they can identify each other remotely. This paper surveys the range of applications of tamper-resistant hardware and the array of attack and defense mechanisms which have evolved in the tamper-resistance arms race.
Pharmacogenomics knowledge for personalized medicine.

The Pharmacogenomics Knowledgebase (PharmGKB) is a resource that collects, curates, and disseminates information about the impact of human genetic variation on drug responses. It provides clinically relevant information, including dosing guidelines, annotated drug labels, and potentially actionable gene-drug associations and genotype-phenotype relationships. Curators assign levels of evidence to variant-drug associations using well-defined criteria based on careful literature review. Thus, PharmGKB is a useful source of high-quality information supporting personalized medicine-implementation projects.
A Foldable 3-DOF Parallel Mechanism With Application to a Flat-Panel TV Mounting Device

This paper deals with a new 3-degree-of-freedom (DOF) parallel mechanism for a flat-panel TV mounting device with two rotations and one translation. The most important operational requirements of this device are that it should support the heavy weight of the flat-panel TV and should be foldable to save space between the flat panel and the wall. An asymmetric parallel structure that has three kinematic chains with internal four-bar linkage is proposed to meet such requirements. Kinematic modeling was performed along with actuator sizing. Finally, the mechanism was developed and tested to show its effectiveness as a flat-panel TV mounting device.
Proximity-Fed Circularly Polarized Slotted Patch Antenna for RFID Handheld Reader

A novel, compact X-shaped slotted square-patch antenna is proposed for circularly polarized (CP) radiation. A cross-strip is embedded along the X-shaped slot for a novel proximity-fed technique to produce CP radiation in the UHF band. In the proposed design, two pairs of T-shaped slots are etched orthogonally on the square patch, connected to the center of the X-shaped slot for CP radiation and antenna size reduction. Proper adjustment of the length and coupling gap of the cross-strip will excite two orthogonal modes with 90 ° phase difference for good CP radiation. Simulated and measured results indicate that the proposed structure can achieve circular polarization. A measured impedance bandwidth (VSWR ≤ 2) of about 3.0% (909-937 MHz) and a 3-dB axial-ratio (AR) bandwidth of about 1.3% (917-929 MHz) were obtained.
Developing a successful SemEval task in sentiment analysis of Twitter and other social media texts

We present the development and evaluation of a semantic analysis task that lies at the intersection of two very trendy lines of research in contemporary computational linguistics: (i) sentiment analysis, and (ii) natural language processing of social media text. The task was part of SemEval, the International Workshop on Semantic Evaluation, a semantic evaluation forum previously known as SensEval. P. Nakov Qatar Computing Research Institute, HBKU Tornado Tower, floor 10, P.O. box 5825, Doha, Qatar E-mail: pnakov@qf.org.qa S. Rosenthal Columbia University E-mail: sara@cs.columbia.edu S. Kiritchenko National Research Council Canada, 1200 Montreal Rd., Ottawa, ON, Canada E-mail: Svetlana.Kiritchenko@nrc-cnrc.gc.ca S. Mohammad National Research Council Canada, 1200 Montreal Rd., Ottawa, ON, Canada E-mail: saif.mohammad@nrc-cnrc.gc.ca Z. Kozareva USC Information Sciences Institute, 4676 Admiralty Way, Marina del Rey, CA 90292-6695 E-mail: zornitsa@kozareva.com A. Ritter The Ohio State University E-mail: aritter@cs.washington.edu V. Stoyanov Facebook E-mail: vesko.st@gmail.com X. Zhu National Research Council Canada, 1200 Montreal Rd., Ottawa, ON, Canada E-mail: Xiaodan.Zhu@nrc-cnrc.gc.ca 2 Preslav Nakov et al. The task ran in 2013 and 2014, attracting the highest number of participating teams at SemEval in both years, and there is an ongoing edition in 2015. The task included the creation of a large contextual and message-level polarity corpus consisting of tweets, SMS messages, LiveJournal messages, and a special test set of sarcastic tweets. The evaluation attracted 44 teams in 2013 and 46 in 2014, who used a variety of approaches. The best teams were able to outperform several baselines by sizable margins with improvement across the two years the task has been run. We hope that the long-lasting role of this task and the accompanying datasets will be to serve as a test bed for comparing different approaches, thus facilitating research.
Automatic Annotation of Daily Activity from Smartphone-Based Multisensory Streams

We present a system for automatic annotation of daily experience from multisensory streams on smartphones. Using smartphones as platform facilitates collection of naturalistic daily activity, which is difficult to collect with multiple on-body sensors or array of sensors affixed to indoor locations. However, recognizing daily activities in unconstrained settings is more challenging than in controlled environments: 1) multiples heterogeneous sensors equipped in smartphones are noisier, asynchronous, vary in sampling rates and can have missing data; 2) unconstrained daily activities are continuous, can occur concurrently, and have fuzzy onset and offset boundaries; 3) ground-truth labels obtained from the user’s self-report can be erroneous and accurate only in a coarse time scale. To handle these problems, we present in this paper a flexible framework for incorporating heterogeneous sensory modalities combined with state-of-the-art classifiers for sequence labeling. We evaluate the system with real-life data containing 11721 minutes of multisensory recordings, and demonstrate the accuracy and efficiency of the proposed system for practical lifelogging applications.
Using enterprise systems to realize digital business strategies

Organizations invest in enterprise systems (ESs) with an expectation to share digital information from disparate sources to improve organizational effectiveness. This study aims to examine how organizations realize digital business strategies using an ES. It does so by evaluating the ES data support activities for knowledge creation, particularly how ES data are transformed into corporate knowledge in relevance to business strategies sought. Further, how this knowledge leads to realization of the business benefits. The linkage between establishing digital business strategy, utilization of ES data in decision-making processes, and realized or unrealized benefits provides the reason for this study.  This study develops and utilizes a transformational model of how ES data are transformed into knowledge and results to evaluate the role of digital business strategies in achieving benefits using an ES. Semi-structured interviews are first conducted with ES vendors, consultants and IT research firms to understand the process of ES data transformation for realizing business strategies from their perspective. This is followed by three in-depth cases (two large and one medium-sized organization) who have implemented ESs. The empirical data are analyzed using the condensation approach. This method condenses the data into multiple groups according to pre-defined categories, which follow the scope of the research questions.  The key findings emphasize that strategic benefit realization from an ES implementation is a holistic process that not only includes the essential data and technology factors, but also includes factors such as digital business strategy deployment, people and process management, and skills and competency development. Although many companies are mature with their ES implementation, these firms have only recently started aligning their ES capabilities with digital business strategies correlating data, decisions, and actions to maximize business value from their ES investment.  The findings reflect the views of two large and one mediumsized organization in the manufacturing sector. Although the evidence of the benefit realization process success and its results is more prominent in larger organizations than medium-sized, it may not be generalized that smaller firms cannot achieve these results. Exploration of these aspects in smaller firms or a different industry sector such as retail/service would be of value.  The paper highlights the importance of tools and practices for accessing relevant information through an integrated ES so that competent decisions can be established towards achieving digital business strategies, and optimizing organizational performance. Knowledge is a key factor in this process.  The paper evaluates a holistic framework for utilization of ES data in realizing digital business strategies. Thus, it develops an enhanced transformational cycle model for ES data transformation into knowledge and results, which maintains to build up the transformational process success in the long term.
Computer Communication Within Industrial Distributed Environment—a Survey

Nowadays, computer systems are presented in almost all types of human activity and they support any kind of industry as well. Most of these systems are distributed where the communication between nodes is based on computer networks of any kind. Connectivity between system components is the key issue when designing distributed systems, especially systems of industrial informatics. The industrial area requires a wide range of computer communication means, particularly time-constrained and safety-enhancing ones. From fieldbus and industrial Ethernet technologies through wireless and internet-working solutions to standardization issues, there are many aspects of computer networks uses and many interesting research domains. Lots of them are quite sophisticated or even unique. The main goal of this paper is to present the survey of the latest trends in the communication domain of industrial distributed systems and to emphasize important questions as dependability, and standardization. Finally, the general assessment and estimation of the future development is provided. The presentation is based on the abstract description of dataflow within a system.
Long short-term memory RNN for biomedical named entity recognition

Biomedical named entity recognition(BNER) is a crucial initial step of information extraction in biomedical domain. The task is typically modeled as a sequence labeling problem. Various machine learning algorithms, such as Conditional Random Fields (CRFs), have been successfully used for this task. However, these state-of-the-art BNER systems largely depend on hand-crafted features. We present a recurrent neural network (RNN) framework based on word embeddings and character representation. On top of the neural network architecture, we use a CRF layer to jointly decode labels for the whole sentence. In our approach, contextual information from both directions and long-range dependencies in the sequence, which is useful for this task, can be well modeled by bidirectional variation and long short-term memory (LSTM) unit, respectively. Although our models use word embeddings and character embeddings as the only features, the bidirectional LSTM-RNN (BLSTM-RNN) model achieves state-of-the-art performance — 86.55% F1 on BioCreative II gene mention (GM) corpus and 73.79% F1 on JNLPBA 2004 corpus. Our neural network architecture can be successfully used for BNER without any manual feature engineering. Experimental results show that domain-specific pre-trained word embeddings and character-level representation can improve the performance of the LSTM-RNN models. On the GM corpus, we achieve comparable performance compared with other systems using complex hand-crafted features. Considering the JNLPBA corpus, our model achieves the best results, outperforming the previously top performing systems. The source code of our method is freely available under GPL at https://github.com/lvchen1989/BNER .
Social Popularity Score: Predicting Numbers of Views, Comments, and Favorites of Social Photos Using Only Annotations

In this paper, we propose an algorithm to predict the social popularity (i.e., the numbers of views, comments, and favorites) of content on social networking services using only text annotations. Instead of analyzing image/video content, we try to estimate social popularity by a combination of weight vectors obtained from a support vector regression (SVR) and tag frequency. Since our proposed algorithm uses text annotations instead of image/video features, its computational cost is small. As a result, we can estimate social popularity more efficiently than previously proposed methods. Furthermore, tags that significantly affect social popularity can be extracted using our algorithm. Our experiments involved using one million photos on the social networking website Flickr, and the results showed a high correlation between actual social popularity and the determination thereof using our algorithm. Moreover, the proposed algorithm can achieve high classification accuracy with regard to a classification between popular and unpopular content.