nils-herrmann/clean_scirepeval huggingface dataset:
C-VQA: A Compositional Split of the Visual Question Answering (VQA) v1.0 Dataset

Visual Question Answering (VQA) has received a lot of attention over the past couple of years. A number of deep learning models have been proposed for this task. However, it has been shown [1–4] that these models are heavily driven by superficial correlations in the training data and lack compositionality – the ability to answer questions about unseen compositions of seen concepts. This compositionality is desirable and central to intelligence. In this paper, we propose a new setting for Visual Question Answering where the test question-answer pairs are compositionally novel compared to training question-answer pairs. To facilitate developing models under this setting, we present a new compositional split of the VQA v1.0 [5] dataset, which we call Compositional VQA (C-VQA). We analyze the distribution of questions and answers in the C-VQA splits. Finally, we evaluate several existing VQA models under this new setting and show that the performances of these models degrade by a significant amount compared to the original VQA setting.
The testing effect and the retention interval: questions and answers.

People learn from tests. Providing tests often enhances retention more than additional study opportunities, but is this testing effect mediated by processes related to retrieval that are fundamentally different from study processes? Some previous studies have reported that testing enhances retention relative to additional studying, but only after a relatively long retention interval. To the extent that this interaction with retention interval dissociates the effects of studying and testing, it may provide crucial evidence for different underlying processes. However, these findings can be questioned because of methodological differences between the study and the test conditions. In two experiments, we eliminated or minimized the confounds that rendered the previous findings equivocal and still obtained the critical interaction. Our results strengthen the evidence for the involvement of different processes underlying the effects of studying and testing, and support the hypothesis that the testing effect is grounded in retrieval-related processes.
Digital Transformation in Latecomer Industries: CIO and CEO Leadership Lessons from Encana Oil & Gas (USA) Inc

Unlike digitally savvy banking and retail industries, oil and natural gas businesses are latecomers to digitization. With large capital investments in complex industrial operations, firms in latecomer industries are seeking ways to cut costs and become responsive to market demands. Executives in oil and natural gas companies are facing unprecedented pressure to cut costs due to market turbulence and need advice on how to undertake digitization; organizational changes needed; who should lead the digitization effort; and the role of the chief information officer (CIO) in executing a digital strategy.
Classification of Atrial Arrhythmias using Neural Networks

2018 Revised  Accepted May 21, 2018 One of the biomedical image problems is the appearance of the bubbles in the slide that could occur when air passes through the slide during the preparation process. These bubbles may complicate the process of analysing the histopathological images.  The objective of this study is to remove the bubble noise from the histopathology images, and then predict the tissues that underlie it using the fuzzy controller in cases of remote pathological diagnosis.  Fuzzy logic uses the linguistic definition to recognize the relationship between the input and the activity, rather than using difficult numerical equation. Mainly there are five parts, starting with accepting the image, passing through removing the bubbles, and ending with predict the tissues. These were implemented by defining membership functions between colours range using MATLAB.  50 histopathological images were tested on four types of membership functions (MF); the results show that (nine-triangular) MF get 75.4% correctly predicted pixels versus 69.1, 72.31 and 72% for (five-triangular), (five-Gaussian) and (nine-Gaussian) respectively.  In line with the era of digitally driven epathology, this process is essentially recommended to ensure quality interpretation and analyses of the processed slides; thus overcoming relevant limitations.
Precision agriculture robot for seeding function

In recent years, robotics in agriculture sector with its implementation based on precision agriculture concept is the newly emerging technology. The main reason behind automation of farming processes are saving the time and energy required for performing repetitive farming tasks and increasing the productivity of yield by treating every crop individually using precision farming concept. Designing of such robots is modeled based on particular approach and certain considerations of agriculture environment in which it is going to work. These considerations and different approaches are discussed in this paper. Also, prototype of an autonomous Agriculture Robot is presented which is specifically designed for seed sowing task only. It is a four wheeled vehicle which is controlled by LPC2148 microcontroller. Its working is based on the precision agriculture which enables efficient seed sowing at optimal depth and at optimal distances between crops and their rows, specific for each crop type.
Learning Discriminative Recommendation Systems with Side Information

Top-N recommendation systems are useful in many real world applications such as E-commerce platforms. Most previous methods produce top-N recommendations based on the observed user purchase or recommendation activities. Recently, it has been noticed that side information that describes the items can be produced from auxiliary sources and help to improve the performance of top-N recommendation systems; e.g., side information of the items can be collected from the item reviews. In this paper, we propose a joint discriminative prediction model that exploits both the partially observed user-item recommendation matrix and the item-based side information to build top-N recommendation systems. This joint model aggregates observed user-item recommendation activities to produce the missing user-item recommendation scores while simultaneously training a linear regression model to predict the user-item recommendation scores from auxiliary item features. We evaluate the proposed approach on a number of recommendation datasets. The experimental results show that the proposed joint model is very effective for producing top-N recommendation systems.
Anonymous Shopping in the Internet by Separation of Data

Whenever clients shop in the Internet, they provide identifying data of themselves to parties like the webshop, shipper and payment system. These identifying data merged with their shopping history might be misused for targeted advertisement up to possible manipulations of the clients. The data also contains credit card or bank account numbers, which may be used for unauthorized money transactions by the involved parties or by criminals hacking the parties’ computing infrastructure. In order to minimize these risks, we propose an approach for anonymous shopping by separation of data. We argue for the feasibility of our approach by discussing important operations like simple reclamation cases and criminal investigations.
Credibility ranking of tweets during high impact events

Twitter has evolved from being a conversation or opinion sharing medium among friends into a platform to share and disseminate information about current events. Events in the real world create a corresponding spur of posts (tweets) on Twitter. Not all content posted on Twitter is trustworthy or useful in providing information about the event. In this paper, we analyzed the credibility of information in tweets corresponding to fourteen high impact news events of 2011 around the globe. From the data we analyzed, on average 30% of total tweets posted about an event contained situational information about the event while 14% was spam. Only 17% of the total tweets posted about the event contained situational awareness information that was credible. Using regression analysis, we identified the important content and sourced based features, which can predict the credibility of information in a tweet. Prominent content based features were number of unique characters, swear words, pronouns, and emoticons in a tweet, and user based features like the number of followers and length of username. We adopted a supervised machine learning and relevance feedback approach using the above features, to rank tweets according to their credibility score. The performance of our ranking algorithm significantly enhanced when we applied re-ranking strategy. Results show that extraction of credible information from Twitter can be automated with high confidence.
The Bitcoin Backbone Protocol with Chains of Variable Difficulty

Bitcoin’s innovative and distributedly maintained blockchain data structure hinges on the adequate degree of difficulty of so-called “proofs of work,” which miners have to produce in order for transactions to be inserted. Importantly, these proofs of work have to be hard enough so that miners have an opportunity to unify their views in the presence of an adversary who interferes but has bounded computational power, but easy enough to be solvable regularly and enable the miners to make progress. As such, as the miners’ population evolves over time, so should the difficulty of these proofs. Bitcoin provides this adjustment mechanism, with empirical evidence of a constant block generation rate against such population changes. In this paper we provide the first (to our knowledge) formal analysis of Bitcoin’s target (re)calculation function in the cryptographic setting, i.e., against all possible adversaries aiming to subvert the protocol’s properties. We extend the q-bounded synchronous model of the Bitcoin backbone protocol [Eurocrypt 2015], which posed the basic properties of Bitcoin’s underlying blockchain data structure and shows how a robust public transaction ledger can be built on top of them, to environments that may introduce or suspend parties in each round. We provide a set of necessary conditions with respect to the way the population evolves under which the “Bitcoin backbone with chains of variable difficulty” provides a robust transaction ledger in the presence of an actively malicious adversary controlling a fraction of the miners strictly below 50% at each instant of the execution. Our work introduces new analysis techniques and tools to the area of blockchain systems that may prove useful in analyzing other blockchain protocols. Part of this work was done while the authors were visiting the Simons Institute for the Theory of Computing, supported by the Simons Foundation and by the DIMACS/Simons Collaboration in Cryptography through NSF grant #CNS-1523467. Research partly supported by ERC project CODAMODA, No. 259152, and Horizon 2020 project PANORAMIX, No. 653497.
Quadrotor Helicopter Flight Dynamics and Control : Theory and Experiment ∗

Quadrotors are rapidly emerging as a popular platform for unmanned aerial vehicle (UAV) research, due to the simplicity of their construction and maintenance, their ability to hover, and their vertical take off and landing (VTOL) capability. Current designs have often considered only nominal operating conditions for vehicle control design. This work seeks to address issues that arise when deviating significantly from the hover flight regime. Aided by well established research for helicopter flight control, four separate aerodynamic effects are investigated as they pertain to quadrotor flight. The effects result from either translational or vertical vehicular velocity components, and cause both moments that affect attitude control and thrust variation that affects altitude control. Where possible, a theoretical development is first presented, and is then validated through both thrust test stand measurements and vehicle flight tests using the Stanford Testbed of Autonomous Rotorcraft for Multi-Agent Control (STARMAC) quadrotor helicopter. The results have enabled improved controller tracking throughout the flight envelope, including at higher speeds and in gusting winds.
Network Anomaly Detection System: The State of Art of Network Behaviour Analysis

This paper presents a taxonomy of anomaly detection techniques that is then used to survey and classify a number of research prototypes and commercial products. Commercial products and solutions based anomaly detection techniques are beginning to establish themselves in mainstream security solutions alongside firewalls, intrusion prevention systems and network monitoring solutions. These solutions are focused mainly on network-based anomaly detection, thus creating a new industry buzzword that describes it: Network Behavior Analysis. This classification is used predictably, pointing towards a number of areas of future research in the field of anomaly detection.
Long Short-Term Memory for Japanese Word Segmentation

This study presents a Long Short-Term Memory (LSTM) neural network approach to Japanese word segmentation (JWS). Previous studies on Chinese word segmentation (CWS) succeeded in using recurrent neural networks such as LSTM and gated recurrent units (GRU). However, in contrast to Chinese, Japanese includes several character types, such as hiragana, katakana, and kanji, that produce orthographic variations and increase the difficulty of word segmentation. Additionally, it is important for JWS tasks to consider a global context, and yet traditional JWS approaches rely on local features. In order to address this problem, this study proposes employing an LSTMbased approach to JWS. The experimental results indicate that the proposed model achieves state-of-the-art accuracy with respect to various Japanese corpora.
Productivity and Schematicity in Metaphors

The theory of metaphor proposed by Lakoff and Johnson (1980a, 1980b) and Lakoff (1993) involves a mapping of conceptual structure from one semantic domain to another. We investigate properties of these conceptual domain mappings by comparing them to morphological derivational relations. Schematicity and productivity are properties that Bybee (1985) and Langacker (1987) propose for characterizing morphological derivational relations, which we apply to our analysis of metaphor. Metaphors are argued to vary in their degree of semantic schematicity: Domain relations function as generalizations over specific metaphorical expressions. We also demonstrate three points on a continuum of productivity: Conventional metaphors are highly productive, metaphorically motivated transparent idioms are semiproductive, and opaque idioms are unproductive. Each point is compared with an example of morphological productivity having a corresponding conceptual organization. The results demonstrate that semantic productivity can be characterized in the same way as morphological productivity, suggesting that form and meaning are organized by the same principles.
Literature Survey on Sentiment Analysis of Twitter Data using Machine Learning Approaches

In today’s world, micro-blogging sites has become a platform for individuals or organizations across the world to express their opinions, sentiment and experience in the form of tweets, status updates, blog posts, etc. This platform has no political and economic restrictions. This paper discusses an approach where a published stream of tweets on electronic products from the twitter micro-blogging site are then subjected to preprocessing and classified based on their emotional content as positive, negative and neutral. The performance of the unsupervised algorithm is then analyzed. The paper concludes with the comparison of the existing system with the proposed systems and applications of the research.
An Enhanced Symmetric Key Cryptography Algorithm to Improve Data Security

This work uses enhanced symmetric key encryption algorithm, in which same structure of encryption and decryption procedure algorithm is used. In conventional encryption methods the key for encryption and decryption is same and remain secret. The algorithm uses key generation method by random number in algorithm for increasing efficiency of algorithm. The algorithm use key size of 512 bits for providing better security and it also provide the concept of internal key generation at receiver end on the basis of 512 bits key which will entered by the sender. This internal key will store in the sender end database and send to the receiver end by other path for preventing brute force attack and other harmful attacks on security. This algorithm is more efficient for large data where existing algorithms provides efficient encryption and decryption only for 2MB data. This work provides better speed in comparison to existing algorithms for large size of files with less overhead.
Constraints on Hypothesis Selection in Causal Learning

How do children identify promising hypotheses worth testing? Many studies have shown that preschoolers can use patterns of covariation together with prior knowledge to learn causal relationships. However, covariation data are not always available and myriad hypotheses may be commensurate with substantive knowledge about content domains. We propose that children can identify high-level abstract features common to effects and their candidate causes and use these to guide their search. We investigate children’s sensitivity to two such high-level features — proportion and dynamics, and show that preschoolers can use these to link effects and candidate causes, even in the absence of other disambiguating information.
Multimodal Network Alignment

A multimodal network encodes relationships between the same set of nodes in multiple settings, and network alignment is a powerful tool for transferring information and insight between a pair of networks. We propose a method for multimodal network alignment that computes a matrix which indicates the alignment, but produces the result as a low-rank factorization directly. We then propose new methods to compute approximate maximum weight matchings of low-rank matrices to produce an alignment. We evaluate our approach by applying it on synthetic networks and use it to de-anonymize a multimodal transportation network.
Intranasal naloxone and related strategies for opioid overdose intervention by nonmedical personnel: a review

Deaths due to prescription and illicit opioid overdose have been rising at an alarming rate, particularly in the USA. Although naloxone injection is a safe and effective treatment for opioid overdose, it is frequently unavailable in a timely manner due to legal and practical restrictions on its use by laypeople. As a result, an effort spanning decades has resulted in the development of strategies to make naloxone available for layperson or "take-home" use. This has included the development of naloxone formulations that are easier to administer for nonmedical users, such as intranasal and autoinjector intramuscular delivery systems, efforts to distribute naloxone to potentially high-impact categories of nonmedical users, as well as efforts to reduce regulatory barriers to more widespread distribution and use. Here we review the historical and current literature on the efficacy and safety of naloxone for use by nonmedical persons, provide an evidence-based discussion of the controversies regarding the safety and efficacy of different formulations of take-home naloxone, and assess the status of current efforts to increase its public distribution. Take-home naloxone is safe and effective for the treatment of opioid overdose when administered by laypeople in a community setting, shortening the time to reversal of opioid toxicity and reducing opioid-related deaths. Complementary strategies have together shown promise for increased dissemination of take-home naloxone, including 1) provision of education and training; 2) distribution to critical populations such as persons with opioid addiction, family members, and first responders; 3) reduction of prescribing barriers to access; and 4) reduction of legal recrimination fears as barriers to use. Although there has been considerable progress in decreasing the regulatory and legal barriers to effective implementation of community naloxone programs, significant barriers still exist, and much work remains to be done to integrate these programs into efforts to provide effective treatment of opioid use disorders.
Chapter 2 Systems Thinking Approach to Robotics Curriculum in Schools

This chapter presents a systems thinking approach for the conceptualization, design, and implementation of robotics curriculum to scaffold students’ learning of important Science, Technology, Engineering, and Mathematics (STEM) concepts and processes. This approach perceives the curriculum as a system of integrated elements and allows for the investigation of the interdependencies amongst the elements and the dynamics of the curriculum as a whole. Through this approach, we believe that students can be provided with robotics curriculum units that facilitate the learning of STEM “Big Ideas” of and about STEM. A STEM “Big Idea” is central to the understanding and application of STEM across a wide range of fields, one that links numerous STEM discipline understandings. Robotics is a rich context in which students can establish deep knowledge and robust understanding of STEM “Big Ideas”. Curriculum units based on this systems thinking approach can do much to ensure that students engaged in robotics activities focus not only on the completion of robotics tasks but also on the social construction of integrated networks of authentic STEM knowledge centred around “Big Ideas” of and about STEM.
Test-enhanced learning: taking memory tests improves long-term retention.

Taking a memory test not only assesses what one knows, but also enhances later retention, a phenomenon known as the testing effect. We studied this effect with educationally relevant materials and investigated whether testing facilitates learning only because tests offer an opportunity to restudy material. In two experiments, students studied prose passages and took one or three immediate free-recall tests, without feedback, or restudied the material the same number of times as the students who received tests. Students then took a final retention test 5 min, 2 days, or 1 week later. When the final test was given after 5 min, repeated studying improved recall relative to repeated testing. However, on the delayed tests, prior testing produced substantially greater retention than studying, even though repeated studying increased students' confidence in their ability to remember the material. Testing is a powerful means of improving learning, not just assessing it.