nils-herrmann/clean_scirepeval huggingface dataset:
Supersizing self supervision: Learning to grasp from 50K tries and 700 robot hours
Venue: 2016 IEEE International Conference on Robotics and Automation (ICRA)

Current model free learning based robot grasping approaches exploit human labeled datasets for training the models. However, there are two problems with such a methodology: (a) since each object can be grasped in multiple ways, manually labeling grasp locations is not a trivial task; (b) human labeling is biased by semantics. While there have been attempts to train robots using trial and error experiments, the amount of data used in such experiments remains substantially low and hence makes the learner prone to over fitting. In this paper, we take the leap of increasing the available training data to 40 times more than prior work, leading to a dataset size of 50K data points collected over 700 hours of robot grasping attempts. This allows us to train a Convolutional Neural Network (CNN) for the task of predicting grasp locations without severe overfitting. In our formulation, we recast the regression problem to an 18 way binary classification over image patches. We also present a multi stage learning approach where a CNN trained in one stage is used to collect hard negatives in subsequent stages. Our experiments clearly show the benefit of using large scale datasets (and multi stage training) for the task of grasping. We also compare to several baselines and show state of the art performance on generalization to unseen objects for grasping.
Protecting Ideas: Ethical and Legal Considerations When a Grant's Principal Investigator Changes
Venue: Sci. Eng. Ethics

Ethical issues related the responsible conduct of research involve questions concerning the rights and obligations of investigators to propose, design, implement, and publish research. When a principal investigator (PI) transfers institutions during a grant cycle, financial and recognition issues need to be addressed to preserve all parties' obligations and best interests in a mutually beneficial way. Although grants often transfer with the PI, sometimes they do not. Maintaining a grant at an institution after the PI leaves does not negate the grantee institution's obligation to recognize the PI's original ideas, contributions, and potential rights to some forms of expression and compensation. Issues include maintaining a role for the PI in determining how to take credit for, share and publish results that involve his or her original ideas. Ascribing proper credit can become a thorny issue. This paper provides a framework for addressing situations and disagreements that may occur when a new PI continues the work after the original PI transfers. Included are suggestions for proactively developing institutional mechanisms that address such issues. Considerations include how to develop solutions that comply with the responsible conduct of research, equitably resolve claims regarding reporting of results, and avoid the possibility of plagiarism.
Ethical Considerations in Research Participation Virality
Venue: Journal of empirical research on human research ethics JERHRE

This article seeks to commence and encourage discussion around the upcoming ethical challenges of virality in network structures. When the call for participation in a research project on lupus in Ireland went from an advertisement in a newsletter to a meme (unit of transmissible information) on a closed Facebook page, the ethical considerations of virality were raised. The article analyzes the Association of Internet Researchers guidelines, Facebook policies, and the context of privacy in relation to virality. Virality creates the leverage for methodological pluralism. The nature of the inquiry can determine the method rather than the other way around. Viral ethical considerations are evolving due to the cyber world becoming the primary meme of communication, with flexibility in the researcher's protocol providing opportunities for efficient, cost effective, and diverse recruitment.
A Dual Band Circularly Polarized Planar Monopole Antenna for WLAN/Wi Fi Applications
Venue: IEEE Antennas and Wireless Propagation Letters

This letter presents a dual band circularly polarized planar monopole antenna. The antenna consists of a radiator, a ground plane, and a microstrip line. The radiator is composed of a "C" shaped strip and an "L" shaped strip. The "C" shaped strip is mainly used for the higher band, and the "L" shaped strip for the lower band. The overall dimension of the prototype is only 40 x47 x1.5 mm3. The proposed antenna has been built and tested. Its measured 10 dB impedance bandwidths are 380 MHz (2.32 2.70 GHz) in the lower band and 1240 MHz (4.76 6 GHz) in the higher band. Its measured 3 dB axial ratio (AR) bandwidths in the broadside direction are 180 MHz (2.39 2.57 GHz) in the lower band and 870 MHz (5.13 6 GHz) in the higher band. The overlapped impedance and AR bandwidths can fully cover all the 2.4/5.2/5.8 GHz WLAN bands and all the 2.4/5.5 GHz Wi Fi bands. The patterns have the characteristic of bidirectional radiation, and the gains are stable in both bands.
What Makes Sports Fans Interactive? Identifying Factors Affecting Chat Interactions in Online Sports Viewing
Venue: PloS one

Sports fans are able to watch games from many locations using TV services while interacting with other fans online. In this paper, we identify the factors that affect sports viewers' online interactions. Using a large scale dataset of more than 25 million chat messages from a popular social TV site for baseball, we extract various game related factors, and investigate the relationships between these factors and fans' interactions using a series of multiple regression analyses. As a result, we identify several factors that are significantly related to viewer interactions. In addition, we determine that the influence of these factors varies according to the user group; i.e. active vs. less active users, and loyal vs. non loyal users.
Adolescents' Perception of the Psychosocial Factors affecting Sustained Engagement in Sports and Physical Activity
Venue: International journal of exercise science

The purpose of this study was to explore adolescents' perceptions of psychosocial influences personal characteristics, environmental factors and behavioural undertakings influencing their prolonged involvement in sports and physical activity (PA) A qualitative approach was adopted wherein 16 adolescents (8 boys, 8 girls; mean age 15.9 years) who had been physically active for at least the last 8 years, and sixteen adults identified as their 'parents' or 'guardians' participated in semi structured interviews. Interviews were transcribed verbatim and coded using the HyperRESEARCH software. Data were analysed using thematic analysis procedures. Four main themes pertaining to psychosocial influences were identified: 1) personal characteristics; 2) school and community resources; 3) parental support; and 4) social interaction. Except for social interaction, for which participants did not identify challenges, themes are discussed according to their motivational aspects and the challenges they represent for adolescents' PA involvement. The research has implications for health promotion endeavours directed toward parents of children and adolescents. Given the limitations of a qualitative study, readers are invited to apply the conclusions to their own context.
Hacking the brain: brain computer interfacing technology and the ethics of neurosecurity
Venue: Ethics and Information Technology

Brain computer interfacing technologies are used as assistive technologies for patients as well as healthy subjects to control devices solely by brain activity. Yet the risks associated with the misuse of these technologies remain largely unexplored. Recent findings have shown that BCIs are potentially vulnerable to cybercriminality. This opens the prospect of "neurocrime" extending the range of computer crime to neural devices. This paper explores a type of neurocrime that we call brain hacking as it aims at the illicit access to and manipulation of neural information and computation. As neural computation underlies cognition, behavior and our self determination as persons, a careful analysis of the emerging risks of malicious brain hacking is paramount, and ethical safeguards against these risks should be considered early in design and regulation. This contribution is aimed at raising awareness of the emerging risk of malicious brain hacking and takes a first step in developing an ethical and legal reflection on those risks.
Brain Computer Interfacing for multimedia quality assessment
Venue: 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

The assessment of perceived multimedia quality is a central research field in information and media technology. Conventionally, psychophysical techniques are used for determining the quality of multimedia signals. Recently, Brain Computer Interfacing (BCI) based methods have been proposed for the assessment of perceived multimedia signal quality. In this paper we give an overview over the shortcomings of conventional approaches, present the state of the art of BCI based methods and discuss open questions and challenges relevant to the BCI community.
Recent advances and open challenges in hybrid brain computer interfacing: a technological review of non invasive human research

In recent years, hybrid brain computer interfaces (hBCIs) have proven to be a promising path towards practical brain computer interfacing. These hybrid interfaces capitalize on the concurrent recording of various physiological signals, or of the elicitation of more than one mental process, to increase the number of possible input commands and achieve more flexible and robust systems. Although hBCIs have previously been reviewed in some articles, a more recent, and complete survey of the literature is missing to lay the foundations for further research.  This work aims at systematically reviewing recent articles on the topic of non invasive hBCIs, to comprehensively identify the current trends, limitations and challenges that these studies report.  Three major databases covering the fields of science and engineering were queried. From these and others sources, 55 journal articles from 2008 to November 2014 were selected and analyzed.  Twenty two items were investigated to o.
Robotic Arm with Brain Computer Interfacing

is a modern technology which is currently revolutionizing the field of signal processing. BCI helped in the evolution of a new world where man and computer had never been so close. Advancements in cognitive neuro sciences facilitated us with better brain imaging techniques and thus interfaces between machines and the human brain became a reality. Electroencephalography (EEG) which is the measurement and recording of electric signals using sensors arrayed across the scalp can be used for applications like prosthetic devices, applications in warfare, gaming, virtual reality and robotics upon signal conditioning and processing. This paper is entirely based on Brain Computer Interface with an objective of actuating a robotic arm with the help of device commands derived from EEG signals. This system unlike any other existing technology is purely non invasive in nature, cost effective and is one of its kinds that can serve various requirements such as prosthesis. This paper suggests a low cost system implementation that can even serve as a reliable substitute for the existing technologies of prosthesis like BIONICS.
An Analysis of Reflectivity and Response Time by Charge to Mass of Charged Particles in an Electrophoretic Display

A reflective electronic display that uses negatively and positively charged particles has excellent bistability, a welldefined threshold voltage, and an extremely fast response time in comparison with other reflective displays. This type of display shows images through the movement of charged particles whose motion depends on the value of q/m (charge per mass for a particle) However, the ratio q/m can easily be changed by the forces acting on the charged particles in a cell of the panel and by friction that occurs after mixing oppositely charged particles and in the particle insertion process. In this study, we propose a method to determine the appropriate range of q/m by using the reflectivity and response time of charged particles to modify q/m. In this manner, the electrical and optical properties of reflective displays are improved.
The Urban EFL Teachers Motivation in Indonesia Maslow s Hierarchy of Needs Theory and Herzberg s Motivational Hygiene Theory

This paper aims at highlighting the urban EFL teachers' motivation in Indonesia viewed from Maslow's hierarchy of needs theory and Herzberg's motivational hygiene theory to fill a notable gap in the teacher motivation literature. Library research is used to review how motivation is essential in English teaching and learning process. The issue: teachers' motivation, conceptualized as job commitment and satisfaction and its relationship to teacher competency will be critically addressed. Commitment and satisfaction to teaching reflects the teachers' motivation as it is theorized that teachers to be committed and satisfied to their job based on the five levels given by Maslow's theory and Herzberg's motivational hygiene as the teachers' motivation affect the teachers' performance in the teaching and learning process. Teachers should be given the motivational factors that will positively influence their performance and productivity which will enhance quality assurance in the educational system. It can be concluded that teachers' motivation is a crucial factor which directly influences the teachers' performance, the level of student motivation and achievement in the target language concerned.
Financial technical indicator based on chaotic bagging predictors for adaptive stock selection in Japanese and American markets

In order to examine the predictability and profitability of financial markets, we introduce three ideas to improve the traditional technical analysis to detect investment timings more quickly. Firstly, a nonlinear prediction model is considered as an effective way to enhance this detection power by learning complex behavioral patterns hidden in financial markets. Secondly, the bagging algorithm can be applied to quantify the confidence in predictions and compose new technical indicators. Thirdly, we also introduce how to select more profitable stocks to improve investment performance by the two step selection: the first step selects more predictable stocks during the learning period, and then the second step adaptively and dynamically selects the most confident stock showing the most significant technical signal in each investment. Finally, some investment simulations based on real financial data show that these ideas are successful in overcoming complex financial markets.
Adoption Intention on Cloud Storage Services: the Role of Technology Trust, Privacy and Security Concerns
Venue: PACIS

Cloud storage is getting increasing attention in the last few years. By moving data to locationtransparent centralized facilities or providers, cloud storage services offer significant economic advantages to consumers and enterprises. However, concerns have been raised regarding the duplication, dissemination, and deletion of data stored on the cloud. In addition, data leakage would be another major concern whether accidental or due to a malicious hacker attack. Thus, the formation of trust in technology is particularly essential for users to cope with the uncertainty of information privacy because users relinquish their ultimate control over the fate of their data. Trust in technology involves two major trusting mechanisms, namely cognitive trust and emotional trust. Cognitive trust, which is also known as trusting belief, refers to the users' rational expectations that the technology under scrutiny will have the necessary attributes to rely on. Three features of cognitive trust have been considered as essential elements for cloud storage applications namely, openness, consent, and access. Meanwhile, emotional trust refers to users' feeling of security and comfort to rely on the technology in use. It contains three different components, namely competence, relatedness, and autonomy. A model is proposed to depict the relationship between technology trust, perceived information privacy and security concerns. This research extends trust research on information privacy and information security concerns and enrich existing literature on the formation of trust. The results of this research could provide specific technology traits that vendors can adopt in to build up users' trust in the technology.
Security Handwritten Documents Using Inner Product

Recently, it is common to find document forgery both in the private and government agencies. This research was conducted with the aim of designing a software system that can recognize hand writing by using computer technology from the digitization process of a written form. The method applied in this study consisted of several stages, namely the writing stage analog to digital conversion, pre processing, segmentation, feature extraction writing and calculating the percentage of similarity algorithms written by Inner product. From the results of this study, it is expected that the system is able to recognize any handwriting owners of various writing forms, as well as to take into account the percentage of similarity to one another. This system can also be used to protect the owner against counterfeiting handwriting, as an authenticity tool for any form of writing in important documents.
A Survey on Big Data Analytics: Challenges, Open Research Issues and Tools

A huge repository of terabytes of data is generated each day from modern information systems and digital technolo gies such as Internet of Things and cloud computing. Analysis of these massive data requires a lot of efforts at multiple levels to extract knowledge for decision making. Therefore, big data analysis is a current area of research and development. The basic objective of this paper is to explore the potential impact of big data challenges, open research issues, and various tools associated with it. As a result, this article provides a platform to explore big data at numerous stages. Additionally, it opens a new horizon for researchers to develop the solution, based on the challenges and open research issues.
A survey of data visualization tools for analyzing large volume of data in big data platform
Venue: 2016 International Conference on Communication and Electronics Systems (ICCES)

The growth of data in the present world is drastically increased, where tons of data is produced from different fields. Due to this enormous growth of data the value of data becomes an important factor in every aspect. The Data exploration and visualization systems play a vital role in the Big Data era. It is a complex task for the companies to explore and visualize very large datasets. Every company should follow some protocol to have accurate insight from analysis of large volume of data. This strategy helps organizations to enhance their process and to find the new product and service opportunities that they may have otherwise missed. In this survey, we explain the major prerequisites and challenges that should be addressed by the recent exploration and visualization systems. It also describes about the different techniques and tools currently used for the visualization of large sets of data and their capabilities to support massive volume of data from variety of data sources.
Dynamic linkages among oil price, gold price, exchange rate, and stock market in India

Governments impose taxes and levies to manage the effect of gold and crude oil imports on the exchange rate. These in return have relations with the economy of the country, best reflected in the stock market index. This study aims to explore the relation between global prices of gold, crude oil, the USD INR exchange rate, and the stock market in India. The dynamic contemporaneous linkages have been analyzed using DCC GARCH (standard, exponential and threshold) models and the lead lag linkages have been examined using symmetric and asymmetric Non Linear Causality tests. Empirical analyses indicate fall in gold prices and crude oil prices cause fall in the value of the Indian Rupee and the benchmark stock index i.e. Sensex. The findings of this study also support the emergence of gold as an investment asset class among the investors. More importantly, this study highlights the need for dynamic policy making in India to contain exchange rate fluctuations and stock market volatility using gold price and oil price as instruments.
IMPACT OF OIL PRICE SHOCKS AND EXCHANGE RATE VOLATILITY ON STOCK MARKET BEHAVIOR IN NIGERIA

The impact of exchange rate and oil prices fluctuation on the stock market has been a subject of hot debate among researchers. This study examined the impact of both the exchange rate volatility and oil price volatility on stock market volatility in Nigeria, so as to guide policy formulation based on the fact that the nation's economy was foreign induced and mono cultured with heavy dependence on oil. EGARCH estimation techniques were employed to examine if either the volatility in exchange rate, oil price volatility or both experts on stock market volatility in Nigeria. The result shows that share price volatility is induced by both the exchange rate volatility and oil price volatility. Thus, it is recommended that policymakers should pursue policies that tend to stabilize the exchange rate regime on the one hand, and guarantee the net oil exporting position for the economy, that market practitioners should formulate portfolio strategies in such a way that volatility in both exchange rates and oil price will be factored in time when investment decisions are being made.
SAR Image Change Detection Based on Multiple Kernel K Means Clustering With Local Neighborhood Information
Venue: IEEE Geoscience and Remote Sensing Letters

Performance of the k means clustering algorithm for synthetic aperture radar (SAR) image change detection is usually worsened by the inherent existence of the speckle noise. Therefore, in this letter, an unsupervised multiple kernel k means clustering algorithm with local neighborhood information (LIMKKM algorithm) is proposed for SAR image change detection. The LIMKKM algorithm contributes in two aspects. First, it fuses various features through a weighted summation kernel by automatically and optimally computing the kernel weights. Here, the intensity and texture features of the ratio image are fused. Second, it incorporates the local neighborhood information into its clustering objective function for providing strong noise immunity. The LIMKKM change detection algorithm is carried out in a train test way to lighten the computational burden. Experimental results on real images demonstrate the effectiveness, especially the strong noise immunity, of the LIMKKM method and illustrate that it is suitable for SAR image change detection.