nils-herrmann/clean_scirepeval huggingface dataset:
Learning to Navigate in Complex Environments

Learning to navigate in complex environments with dynamic elements is an important milestone in developing AI agents. In this work we formulate the navigation question as a reinforcement learning problem and show that data efficiency and task performance can be dramatically improved by relying on additional auxiliary tasks to bootstrap learning. In particular we consider jointly learning the goal-driven reinforcement learning problem with an unsupervised depth prediction task and a self-supervised loop closure classification task. Using this approach we can learn to navigate from raw sensory input in complicated 3D mazes, approaching human-level performance even under conditions where the goal location changes frequently. We provide detailed analysis of the agent behaviour, its ability to localise, and its network activity dynamics. We then show that the agent implicitly learns key navigation abilities, through reinforcement learning with sparse rewards and without direct supervision.
The MemSQL Query Optimizer: A modern optimizer for real-time analytics in a distributed database

Real-time analytics on massive datasets has become a very common need in many enterprises. These applications require not only rapid data ingest, but also quick answers to analytical queries operating on the latest data. MemSQL is a distributed SQL database designed to exploit memory-optimized, scale-out architecture to enable real-time transactional and analytical workloads which are fast, highly concurrent, and extremely scalable. Many analytical queries in MemSQL’s customer workloads are complex queries involving joins, aggregations, subqueries, etc. over star and snowflake schemas, often ad-hoc or produced interactively by business intelligence tools. These queries often require latencies of seconds or less, and therefore require the optimizer to not only produce a high quality distributed execution plan, but also produce it fast enough so that optimization time does not become a bottleneck. In this paper, we describe the architecture of the MemSQL Query Optimizer and the design choices and innovations which enable it quickly produce highly efficient execution plans for complex distributed queries. We discuss how query rewrite decisions oblivious of distribution cost can lead to poor distributed execution plans, and argue that to choose high-quality plans in a distributed database, the optimizer needs to be distribution-aware in choosing join plans, applying query rewrites, and costing plans. We discuss methods to make join enumeration faster and more effective, such as a rewrite-based approach to exploit bushy joins in queries involving multiple star schemas without sacrificing optimization time. We demonstrate the effectiveness of the MemSQL optimizer over queries from the TPC-H benchmark and a real customer workload.
Routing in Internet of Vehicles: A Review

This work aims to provide a review of the routing protocols in the Internet of Vehicles (IoV) from routing algorithms to their evaluation approaches. We provide five different taxonomies of routing protocols. First, we classify them based on their transmission strategy into three categories: unicast, geocast, and broadcast ones. Second, we classify them into four categories based on information required to perform routing: topology-, position-, map-, and path-based ones. Third, we identify them in delay-sensitive and delay-tolerant ones. Fourth, we discuss them according to their applicability in different dimensions, i.e., 1-D, 2-D, and 3-D. Finally, we discuss their target networks, i.e., homogeneous and heterogeneous ones. As the evaluation is also a vital part in IoV routing protocol studies, we examine the evaluation approaches, i.e., simulation and real-world experiments. IoV includes not only the traditional vehicular ad hoc networks, which usually involve a small-scale and homogeneous network, but also a much larger scale and heterogeneous one. The composition of classical routing protocols and latest heterogeneous network approaches is a promising topic in the future. This work should motivate IoV researchers, practitioners, and new comers to develop IoV routing protocols and technologies.
Computational Thinking: What and Why?

In my March 2006 CACM article I used the term " computational thinking " to articulate a vision that everyone, not just those who major in computer science, can benefit from thinking like a computer scientist [Wing06]. So, what is computational thinking? Here is a definition that Jan use; it is inspired by an email exchange I had with Al Aho of Columbia University: Computational Thinking is the thought processes involved in formulating problems and their solutions so that the solutions are represented in a form that can be effectively carried out by an information-processing agent [CunySnyderWing10] Informally, computational thinking describes the mental activity in formulating a problem to admit a computational solution. The solution can be carried out by a human or machine, or more generally, by combinations of humans and machines. When I use the term computational thinking, my interpretation of the words " problem " and " solution " is broad; in particular, I mean not just mathematically well-defined problems whose solutions are completely analyzable, e.g., a proof, an algorithm, or a program, but also real-world problems whose solutions might be in the form of large, complex software systems. Thus, computational thinking overlaps with logical thinking and systems thinking. It includes algorithmic thinking and parallel thinking, which in turn engage other kinds of thought processes, e.g., compositional reasoning, pattern matching, procedural thinking, and recursive thinking. Computational thinking is used in the design and analysis of problems and their solutions, broadly interpreted. The most important and high-level thought process in computational thinking is the abstraction process. Abstraction is used in defining patterns, generalizing from instances, and parameterization. It is used to let one object stand for many. It is used to capture essential properties common to a set of objects while hiding irrelevant distinctions among them. For example, an algorithm is an abstraction of a process that takes inputs, executes a sequence of steps, and produces outputs to satisfy a desired goal. An abstract data type defines an abstract set of values and operations for manipulating those values, hiding the actual representation of the values from the user of the abstract data type. Designing efficient algorithms inherently involves designing abstract data types. Abstraction gives us the power to scale and deal with complexity. Recursively applying abstraction gives us the ability to build larger and larger systems, with the base case (at least for computer science) being bits (0's …
An SVM learning approach to robotic grasping

Finding appropriate stable grasps for a hand (either robotic or human) on an arbitrary object has proved to be a challenging and difficult problem. The space of grasping parameters coupled with the degrees-of-freedom and geometry of the object to be grasped creates a high-dimensional, non-smooth manifold. Traditional search methods applied to this manifold are typically not powerful enough to find appropriate stable grasping solutions, let alone optimal grasps. We address this issue in this paper, which attempts to find optimal grasps of objects using a grasping simulator. Our unique approach to the problem involves a combination of numerical methods to recover parts of the grasp quality surface with any robotic hand, and contemporary machine learning methods to interpolate that surface, in order to find the optimal grasp.
Visual Reinforcement Learning with Imagined Goals

For an autonomous agent to fulfill a wide range of user-specified goals at test time, it must be able to learn broadly applicable and general-purpose skill repertoires. Furthermore, to provide the requisite level of generality, these skills must handle raw sensory input such as images. In this paper, we propose an algorithm that acquires such general-purpose skills by combining unsupervised representation learning and reinforcement learning of goal-conditioned policies. Since the particular goals that might be required at test-time are not known in advance, the agent performs a self-supervised “practice” phase where it imagines goals and attempts to achieve them. We learn a visual representation with three distinct purposes: sampling goals for self-supervised practice, providing a structured transformation of raw sensory inputs, and computing a reward signal for goal reaching. We also propose a retroactive goal relabeling scheme to further improve the sample-efficiency of our method. Our off-policy algorithm is efficient enough to learn policies that operate on raw image observations and goals for a real-world robotic system, and substantially outperforms prior techniques.
Real-Time Automatic Traffic Accident Recognition Using HFG

Recently, the problem of automatic traffic accident recognition has appealed to the machine vision community due to its implications on the development of autonomous Intelligent Transportation Systems (ITS). In this paper, a new framework for real-time automated traffic accidents recognition using Histogram of Flow Gradient (HFG) is proposed. This framework performs two major steps. First, HFG-based features are extracted from video shots. Second, logistic regression is employed to develop a model for the probability of occurrence of an accident by fitting data to a logistic curve. In case of occurrence of an accident, the trajectory of vehicle by which the accident was occasioned is determined. Preliminary results on real video sequences confirm the effectiveness and the applicability of the proposed approach, and it can offer delay guarantees for real-time surveillance and monitoring scenarios.
Detecting Hate Speech in Social Media

In this paper we examine methods to detect hate speech in social media, while distinguishing this from general profanity. We aim to establish lexical baselines for this task by applying supervised classification methods using a recently released dataset annotated for this purpose. As features, our system uses character n-grams, word n-grams and word skip-grams. We obtain results of 78% accuracy in identifying posts across three classes. Results demonstrate that the main challenge lies in discriminating profanity and hate speech from each other. A number of directions for future work are discussed.
Believe it or not: Factors influencing credibility on the Web

This article reviews selected literature related to the credibility of information, including (1) the general markers of credibility, and how different source, message and receiver characteristics affect people’s perceptions of information; (2) the impact of information medium on the assessment of credibility; and (3) the assessment of credibility in the context of information presented on the Internet. The objective of the literature review is to synthesize the current state of knowledge in this area, develop new ways to think about how people interact with information presented via the Internet, and suggest next steps for research and practical applications. The review examines empirical evidence, key reviews, and descriptive material related to credibility in general, and in terms of on-line media. A general discussion of credibility and persuasion and a description of recent work on the credibility and persuasiveness of computer-based applications is presented. Finally, the article synthesizes what we have learned from various fields, and proposes a model as a framework for much-needed future research in this area.
Coherent line drawing

This paper presents a non-photorealistic rendering technique that automatically generates a line drawing from a photograph. We aim at extracting a set of coherent, smooth, and stylistic lines that effectively capture and convey important shapes in the image. We first develop a novel method for constructing a smooth direction field that preserves the flow of the salient image features. We then introduce the notion of flow-guided anisotropic filtering for detecting highly coherent lines while suppressing noise. Our method is simple and easy to implement. A variety of experimental results are presented to show the effectiveness of our method in producing self-contained, high-quality line illustrations.
Detecting Click Fraud in Pay-Per-Click Streams of Online Advertising Networks

With the rapid growth of the Internet, online advertisement plays a more and more important role in the advertising market. One of the current and widely used revenue models for online advertising involves charging for each click based on the popularity of keywords and the number of competing advertisers. This pay-per-click model leaves room for individuals or rival companies to generate false clicks (i.e., click fraud), which pose serious problems to the development of healthy online advertising market. To detect click fraud, an important issue is to detect duplicate clicks over decaying window models, such as jumping windows and sliding windows. Decaying window models can be very helpful in defining and determining click fraud. However, although there are available algorithms to detect duplicates, there is still a lack of practical and effective solutions to detect click fraud in pay-per-click streams over decaying window models. In this paper, we address the problem of detecting duplicate clicks in pay-per-click streams over jumping windows and sliding windows, and are the first that propose two innovative algorithms that make only one pass over click streams and require significantly less memory space and operations. GBF algorithm is built on group Bloom filters which can process click streams over jumping windows with small number of sub-windows, while TBF algorithm is based on a new data structure called timing Bloom filter that detects click fraud over sliding windows and jumping windows with large number of sub-windows. Both GBF algorithm and TBF algorithm have zero false negative. Furthermore, both theoretical analysis and experimental results show that our algorithms can achieve low false positive rate when detecting duplicate clicks in pay-per-click streams over jumping windows and sliding windows.
Latent Cross: Making Use of Context in Recurrent Recommender Systems

The success of recommender systems often depends on their ability to understand and make use of the context of the recommendation request. Significant research has focused on how time, location, interfaces, and a plethora of other contextual features affect recommendations. However, in using deep neural networks for recommender systems, researchers often ignore these contexts or incorporate them as ordinary features in the model.
 In this paper, we study how to effectively treat contextual data in neural recommender systems. We begin with an empirical analysis of the conventional approach to context as features in feed-forward recommenders and demonstrate that this approach is inefficient in capturing common feature crosses. We apply this insight to design a state-of-the-art RNN recommender system. We first describe our RNN-based recommender system in use at YouTube. Next, we offer "Latent Cross," an easy-to-use technique to incorporate contextual data in the RNN by embedding the context feature first and then performing an element-wise product of the context embedding with model's hidden states. We demonstrate the improvement in performance by using this Latent Cross technique in multiple experimental settings.
Modeling public mood and emotion: Twitter sentiment and socio-economic phenomena

We perform a sentiment analysis of all tweets published on the microblogging platform Twitter in the second half of 2008. We use a psychometric instrument to extract six mood states (tension, depression, anger, vigor, fatigue, confusion) from the aggregated Twitter content and compute a six-dimensional mood vector for each day in the timeline. We compare our results to a record of popular events gathered from media and sources. We find that events in the social, political, cultural and economic sphere do have a significant, immediate and highly specific effect on the various dimensions of public mood. We speculate that large scale analyses of mood can provide a solid platform to model collective emotive trends in terms of their predictive value with regards to existing social as well as economic indicators.
Predicting clicks: CTR estimation of advertisements using Logistic Regression classifier

Search engine advertising in the present day is a pronounced component of the Web. Choosing the appropriate and relevant ad for a particular query and positioning of the ad critically impacts the probability of being noticed and clicked. It also strategically impacts the revenue, the search engine shall generate from a particular Ad. Needless to say, showing the user an Ad that is relevant to his/her need greatly improves users satisfaction. For all the aforesaid reasons, its of utmost importance to correctly determine the click-through rate (CTR) of ads in a system. For frequently appearing ads, CTR is empirically measurable, but for the new ads, other means have to be devised. In this paper we propose and establish a model to predict the CTRs of advertisements adopting Logistic Regression as the effective framework for representing and constructing conditions and vulnerabilities among variables. Logistic Regression is a type of probabilistic statistical classification model that predicts a binary response from a binary predictor, based on one or more predictor variables. Advertisements that have the most elevated to be clicked are chosen using supervised machine learning calculation. We tested Logistic Regression algorithm on a one week advertisement data of size around 25 GB by considering position and impression as predictor variables. Using this prescribed model we were able to achieve around 90% accuracy for CTR estimation.
Social media addiction: What is the role of content in YouTube?

YouTube, the online video creation and sharing site, supports both video content viewing and content creation activities. For a minority of people, the time spent engaging with YouTube can be excessive and potentially problematic.  This study analyzed the relationship between content viewing, content creation, and YouTube addiction in a survey of 410 Indian-student YouTube users. It also examined the influence of content, social, technology, and process gratifications on user inclination toward YouTube content viewing and content creation.  The results demonstrated that content creation in YouTube had a closer relationship with YouTube addiction than content viewing. Furthermore, social gratification was found to have a significant influence on both types of YouTube activities, whereas technology gratification did not significantly influence them. Among all perceived gratifications, content gratification had the highest relationship coefficient value with YouTube content creation inclination. The model fit and variance extracted by the endogenous constructs were good, which further validated the results of the analysis.  The study facilitates new ways to explore user gratification in using YouTube and how the channel responds to it.
Applying multimedia design principles enhances learning in medical education.

The Association of American Medical Colleges' Institute for Improving Medical Education's report entitled 'Effective Use of Educational Technology' called on researchers to study the effectiveness of multimedia design principles. These principles were empirically shown to result in superior learning when used with college students in laboratory studies, but have not been studied with undergraduate medical students as participants.



A pre-test/post-test control group design was used, in which the traditional-learning group received a lecture on shock using traditionally designed slides and the modified-design group received the same lecture using slides modified in accord with Mayer's principles of multimedia design. Participants included Year 3 medical students at a private, midwestern medical school progressing through their surgery clerkship during the academic year 2009-2010. The medical school divides students into four groups; each group attends the surgery clerkship during one of the four quarters of the academic year. Students in the second and third quarters served as the modified-design group (n=91) and students in the fourth-quarter clerkship served as the traditional-design group (n=39).



Both student cohorts had similar levels of pre-lecture knowledge. Both groups showed significant improvements in retention (p<0.0001), transfer (p<0.05) and total scores (p<0.0001) between the pre- and post-tests. Repeated-measures anova analysis showed statistically significant greater improvements in retention (F=10.2, p=0.0016) and total scores (F=7.13, p=0.0081) for those students instructed using principles of multimedia design compared with those instructed using the traditional design.



Multimedia design principles are easy to implement and result in improved short-term retention among medical students, but empirical research is still needed to determine how these principles affect transfer of learning. Further research on applying the principles of multimedia design to medical education is needed to verify the impact it has on the long-term learning of medical students, as well as its impact on other forms of multimedia instructional programmes used in the education of medical students.
A Translational Profiling Approach for the Molecular Characterization of CNS Cell Types

The cellular heterogeneity of the brain confounds efforts to elucidate the biological properties of distinct neuronal populations. Using bacterial artificial chromosome (BAC) transgenic mice that express EGFP-tagged ribosomal protein L10a in defined cell populations, we have developed a methodology for affinity purification of polysomal mRNAs from genetically defined cell populations in the brain. The utility of this approach is illustrated by the comparative analysis of four types of neurons, revealing hundreds of genes that distinguish these four cell populations. We find that even two morphologically indistinguishable, intermixed subclasses of medium spiny neurons display vastly different translational profiles and present examples of the physiological significance of such differences. This genetically targeted translating ribosome affinity purification (TRAP) methodology is a generalizable method useful for the identification of molecular changes in any genetically defined cell type in response to genetic alterations, disease, or pharmacological perturbations.
Power saving and energy optimization techniques for Wireless Sensor Networks

Wireless sensor networks have become increasingly popular due to their wide range of applications. Energy consumption is one of the biggest constraints of the wireless sensor node and this limitation combined with a typical deployment of large number of nodes have added many challenges to the design and management of wireless sensor networks. They are typically used for remote environment monitoring in areas where providing electrical power is difficult. Therefore, the devices need to be powered by batteries and alternative energy sources. Because battery energy is limited, the use of different techniques for energy saving is one of the hottest topics in WSNs. In this work, we present a survey of power saving and energy optimization techniques for wireless sensor networks, which enhances the ones in existence and introduces the reader to the most well known available methods that can be used to save energy. They are analyzed from several points of view: Device hardware, transmission, MAC and routing protocols.