nils-herrmann/clean_scirepeval huggingface dataset:
A frontier-based approach for autonomous exploration

We introduce a new approach for exploration based on the concept of frontiers, regions on the boundary between open space and unexplored space. By moving to new frontiers, a mobile robot can extend its map into new territory until the entire environment has been explored. We describe a method for detecting frontiers in evidence grids and navigating to these frontiers. We also introduce a technique for minimizing specular reflections in evidence grids using laser-limited sonar. We have tested this approach with a real mobile robot, exploring real-world office environments cluttered with a variety of obstacles. An advantage of our approach is its ability to explore both large open spaces and narrow cluttered spaces, with walls and obstacles in arbitrary orientations.
Contextualization of topics: browsing through the universe of bibliographic information

This paper describes how semantic indexing can help to generate a contextual overview of topics and visually compare clusters of articles. The method was originally developed for an innovative information exploration tool, called Ariadne, which operates on bibliographic databases with tens of millions of records (Koopman et al. ). In this paper, the method behind Ariadne is further developed and applied to the research question of the special issue “Same data, different results”—the better understanding of topic (re-)construction by different bibliometric approaches. For the case of the Astro dataset of 111,616 articles in astronomy and astrophysics, a new instantiation of the interactive exploring tool, LittleAriadne, has been created. This paper contributes to the overall challenge to delineate and define topics in two different ways. First, we produce two clustering solutions based on vector representations of articles in a lexical space. These vectors are built on semantic indexing of entities associated with those articles. Second, we discuss how LittleAriadne can be used to browse through the network of topical terms, authors, journals, citations and various cluster solutions of the Astro dataset. More specifically, we treat the assignment of an article to the different clustering solutions as an additional element of its bibliographic record. Keeping the principle of semantic indexing on the level of such an extended list of entities of the bibliographic record, LittleAriadne in turn provides a visualization of the context of a specific clustering solution. It also conveys the similarity of article clusters produced by different algorithms, hence representing a complementary approach to other possible means of comparison.
Road anomaly estimation: Model based pothole detection

This paper describes a model-based pothole detection algorithm that exploits a multi-phase dynamic model. The responses of hitting potholes are empirically broken down into three phases governed by three simpler dynamic system sub-models. Each sub-model is based on a rigid-ring tire and quarter-car suspension model. The model is validated by comparing simulation results over various scenarios with FTire, a commercial simulation software for tire-road interaction. Based on the developed model, a pothole detection algorithm with Unscented Kalman Filter (UKF) and Bayesian estimation is developed and demonstrated.
Artistic minimal rendering with lines and blocks

Many non-photorealistic rendering techniques exist to produce artistic ef fe ts from given images. Inspired by various artists, interesting effects can be produced b y using a minimal rendering, where the minimum refers to the number of tones as well as the nu mber and complexity of the primitives used for rendering. Our method is based on va rious computer vision techniques, and uses a combination of refined lines and blocks (po tentially simplified), as well as a small number of tones, to produce abstracted artistic re ndering with sufficient elements from the original image. We also considered a variety of methods to produce different artistic styles, such as colour and two-tone drawing s, and use semantic information to improve renderings for faces. By changing some intuitive par ameters a wide range of visually pleasing results can be produced. Our method is fully automatic. We demonstrate the effectiveness of our method with extensive experiments and a user study.
A Technique for Enabling and Supporting Debugging of Field Failures

It is difficult to fully assess the quality of software inhouse, outside the actual time and context in which it will execute after deployment. As a result, it is common for software to manifest field failures, failures that occur on user machines due to untested behavior. Field failures are typically difficult to recreate and investigate on developer platforms, and existing techniques based on crash reporting provide only limited support for this task. In this paper, we present a technique for recording, reproducing, and minimizing failing executions that enables and supports inhouse debugging of field failures. We also present a tool that implements our technique and an empirical study that evaluates the technique on a widely used e-mail client.
A Comparison of Reading Paper and On-Line Documents

We report on a laboratory study that compares reading from paper to reading on-line. Critical differences have to do with the major advantages paper offers in supporting annotation while reading, quick navigation, and flexibility of spatial layout. These, in turn, allow readers to deepen their understanding of the text, extract a sense of its structure, create a plan for writing, cross-refer to other documents, and interleave reading and writing. We discuss the design implications of these findings for the development of better reading technologies.
A Universal Catalyst for First-Order Optimization

We introduce a generic scheme for accelerating first-order optimization methods in the sense of Nesterov. Our approach consists of minimizing a convex objective by approximately solving a sequence of well-chosen auxiliary problems, leading to faster convergence. This strategy applies to a large class of algorithms, including gradient descent, block coordinate descent, SAG, SAGA, SDCA, SVRG, Finito/MISO, and their proximal variants. For all of these approaches, we provide acceleration and explicit support for non-strongly convex objectives. In addition to theoretical speed-up, we also show that acceleration is useful in practice, especially for ill-conditioned problems where we measure significant improvements.
Location of banking automatic teller machines based on convolution

In this paper, the problem of determining the optimum number and locations of banking automatic teller machines (ATMs) is considered. The objective is to minimize the total number of ATMs to cover all customer demands within a given geographical area. First, a mathematical model of this optimization problem is formulated. A novel heuristic algorithm with unique features is then developed to efficiently solve this problem. Finally, simulation results show the effectiveness of this algorithm in solving the ATM placement problem.
Discovering Author Interest Evolution in Topic Modeling

Discovering the author's interest over time from documents has important applications in recommendation systems, authorship identification and opinion extraction. In this paper, we propose an interest drift model (IDM), which monitors the evolution of author interests in time-stamped documents. The model further uses the discovered author interest information to help finding better topics. Unlike traditional topic models, our model is sensitive to the ordering of words, thus it extracts more information from the semantic meaning of the context. The experiment results show that the IDM model learns better topics than state-of-the-art topic models.
Perceptions of Service Quality in Jordanian Hotels

The importance of service quality for business performance has been recognized in the literature through the direct effect on customer satisfaction and the indirect effect on customer loyalty. The main objective of the study was to measure hotels' service quality performance from the customer perspective. To do so, a performance-only measurement scale (SERVPERF) was administered to customers stayed in three, four and five star hotels in Aqaba and Petra. Although the importance of service quality and service quality measurement has been recognized, there has been limited research that has addressed the structure and antecedents of the concept for the hotel industry. The clarification of the dimensions is important for managers in the hotel industry as it identifies the bundles of service attributes consumers find important. The results of the study demonstrate that SERVPERF is a reliable and valid tool to measure service quality in the hotel industry. The instrument consists of five dimensions, namely "tangibles", "responsiveness", "empathy", "assurance" and "reliability". Hotel customers are expecting more improved services from the hotels in all service quality dimensions. However, hotel customers have the lowest perception scores on empathy and tangibles. In the light of the results, possible managerial implications are discussed and future research subjects are recommended.
System Dynamics Model for Demand Side Management

With the development of power market, demand side management (DSM) should be paid more attention to improve the shape of load curve and the growth of peak load. In the implementation of DSM, the key is how to design electricity price arranged between an electricity supply utility and its customers for providing sufficient financial incentives. This paper utilizes system dynamics theory to establish the dynamic model of demand side management, which consists of internal and external structure among the power demand, the two-part electricity price, the output value of customers, technology development and economic situation based on computer simulation of many differential equations with feedback and time delays. By using this model, the policies of electricity price, such as the ratio of capacity and energy fees in the two-part electricity price, the ratio of peak and valley time price in the time-of-use electricity price are analyzed and proposed
QArabPro: A Rule Based Question Answering System for Reading Comprehension Tests in Arabic

Extensive research efforts in the area of Natural L anguage Processing (NLP) were focused on developing reading comprehens ion Question Answering systems (QA) for Latin based languages such as, English, French and German .  However, little effort was directed towards the development of such systems for bidirec tional languages such as Arabic, Urdu and Farsi. In general, QA systems are more sophisticated and more complex than Search Engines (SE) because they seek a specific and somewhat exact answer to the query.  Existing Arabic QA system including the most recent described excluded one or both types of questions (How and Why) from their work because of the difficulty of handling these questions. In this study, we present a new approach and a new questio nanswering system (QArabPro) for reading comprehensi on texts in Arabic. The overall accuracy of our system is 84%.  These results are promising compared to existing systems. Our system handles all types of questions including (How and why).
Combined Group and Exclusive Sparsity for Deep Neural Networks

The number of parameters in a deep neural network is usually very large, which helps with its learning capacity but also hinders its scalability and practicality due to memory/time inefficiency and overfitting. To resolve this issue, we propose a sparsity regularization method that exploits both positive and negative correlations among the features to enforce the network to be sparse, and at the same time remove any redundancies among the features to fully utilize the capacity of the network. Specifically, we propose to use an exclusive sparsity regularization based on (1, 2)-norm, which promotes competition for features between different weights, thus enforcing them to fit to disjoint sets of features. We further combine the exclusive sparsity with the group sparsity based on (2, 1)-norm, to promote both sharing and competition for features in training of a deep neural network. We validate our method on multiple public datasets, and the results show that our method can obtain more compact and efficient networks while also improving the performance over the base networks with full weights, as opposed to existing sparsity regularizations that often obtain efficiency at the expense of prediction accuracy.
An Empirical Study on Real Bug Fixes

Software bugs can cause significant financial loss and even the loss of human lives. To reduce such loss, developers devote substantial efforts to fixing bugs, which generally requires much expertise and experience. Various approaches have been proposed to aid debugging. An interesting recent research direction is automatic program repair, which achieves promising results, and attracts much academic and industrial attention. However, people also cast doubt on the effectiveness and promise of this direction. A key criticism is to what extent such approaches can fix real bugs. As only research prototypes for these approaches are available, it is infeasible to address the criticism by evaluating them directly on real bugs. Instead, in this paper, we design and develop BugStat, a tool that extracts and analyzes bug fixes. With BugStat's support, we conduct an empirical study on more than 9,000 real-world bug fixes from six popular Java projects. Comparing the nature of manual fixes with automatic program repair, we distill 15 findings, which are further summarized into four insights on the two key ingredients of automatic program repair: fault localization and faulty code fix. In addition, we provide indirect evidence on the size of the search space to fix real bugs and find that bugs may also reside in non-source files. Our results provide useful guidance and insights for improving the state-of-the-art of automatic program repair.
Connotation Frames of Power and Agency in Modern Films

The framing of an action influences how we perceive its actor. We introduce connotation frames of power and agency, a pragmatic formalism organized using frame semantic representations, to model how different levels of power and agency are implicitly projected on actors through their actions. We use the new power and agency frames to measure the subtle, but prevalent, gender bias in the portrayal of modern film characters and provide insights that deviate from the well-known Bechdel test. Our contributions include an extended lexicon of connotation frames along with a web interface that provides a comprehensive analysis through the lens of connotation frames.
Using artificial neural network in intrusion detection systems to computer networks

The constant growth in the use of computer networks has demanded some concerns regarding disponibility, vulnerability and security. Intrusion Detection Systems (IDS) have been considered essential in keeping network security and therefore have been commonly adopted by network administrators. A possible disadvantage is the fact that such systems are usually based on signature systems, which make them strongly dependent on updated database and consequently inefficient against novel attacks (unknown attacks). The research presented in this paper proposes an IDS system based on artificial neural network (ANN) and the KDDCUP'99 dataset. Experimental results clearly show that the proposed system can reach an overall accuracy of 99.9% regarding the classification of pre-defined classes of intrusion attacks with, which is a very satisfactory result when compared to traditional methods.
Ciphertext-Policy Attribute-Based Encryption: An Expressive, Efficient, and Provably Secure Realization

We present a new methodology for realizing Ciphertext-Policy Attribute Encryption (CP-ABE) under concrete and noninteractive cryptographic assumptions in the standard model. Our solutions allow any encryptor to specify access control in terms of any access formula over the attributes in the system. In our most efficient system, ciphertext size, encryption, and decryption time scales linearly with the complexity of the access formula. The only previous work to achieve these parameters was limited to a proof in the generic group model. We present three constructions within our framework. Our first system is proven selectively secure under a assumption that we call the decisional Parallel Bilinear Diffie-Hellman Exponent (PBDHE) assumption which can be viewed as a generalization of the BDHE assumption. Our next two constructions provide performance tradeoffs to achieve provable security respectively under the (weaker) decisional Bilinear-Diffie-Hellman Exponent and decisional Bilinear Diffie-Hellman assumptions.
Combining Word Semantics within Complex Hilbert Space for Information Retrieval

Complex numbers are a fundamental aspect of the mathematical formalism of quantum physics. Quantum-like models developed outside physics often overlooked the role of complex numbers. Specifically, previous models in Information Retrieval (IR) ignored complex numbers. We argue that to advance the use of quantum models of IR, one has to lift the constraint of real-valued representations of the information space, and package more information within the representation by means of complex numbers. As a first attempt, we propose a complex-valued representation for IR, which explicitly uses complex valued Hilbert spaces, and thus where terms, documents and queries are represented as complex-valued vectors. The proposal consists of integrating distributional semantics evidence within the real component of a term vector; whereas, ontological information is encoded in the imaginary component. Our proposal has the merit of lifting the role of complex numbers from a computational byproduct of the model to the very mathematical texture that unifies different levels of semantic information. An empirical instantiation of our proposal is tested in the TREC Medical Record task of retrieving cohorts for clinical studies.
Spacemint: A Cryptocurrency Based on Proofs of Space

Bitcoin has become the most successful cryptocurrency ever deployed, and its most distinctive feature is that it is decentralized. Its underlying protocol (Nakamoto consensus) achieves this by using proof of work, which has the drawback that it causes the consumption of vast amounts of energy to maintain the ledger. Moreover, Bitcoin mining dynamics have become less distributed over time. Towards addressing these issues, we propose SpaceMint, a cryptocurrency based on proofs of space instead of proofs of work. Miners in SpaceMint dedicate disk space rather than computation. We argue that SpaceMint’s design solves or alleviates several of Bitcoin’s issues: most notably, its large energy consumption. SpaceMint also rewards smaller miners fairly according to their contribution to the network, thus incentivizing more distributed participation. This paper adapts proof of space to enable its use in cryptocurrency, studies the attacks that can arise against a Bitcoin-like blockchain that uses proof of space, and proposes a new blockchain format and transaction types to address these attacks. Our prototype shows that initializing 1 TB for mining takes about a day (a one-off setup cost), and miners spend on average just a fraction of a second per block mined. Finally, we provide a game-theoretic analysis modeling SpaceMint as an extensive game (the canonical game-theoretic notion for games that take place over time) and show that this stylized game satisfies a strong equilibrium notion, thereby arguing for SpaceMint’s stability and consensus.
Shirtless and Dangerous: Quantifying Linguistic Signals of Gender Bias in an Online Fiction Writing Community

Imagine a princess asleep in a castle, waiting for her prince to slay the dragon and rescue her. Tales like the famous Sleeping Beauty clearly divide up gender roles. But what about more modern stories, borne of a generation increasingly aware of social constructs like sexism and racism? Do these stories tend to reinforce gender stereotypes, or counter them? In this paper, we present a technique that combines natural language processing with a crowdsourced lexicon of stereotypes to capture gender biases in fiction. We apply this technique across 1.8 billion words of fiction from the Wattpad online writing community, investigating gender representation in stories, how male and female characters behave and are described, and how authors’ use of gender stereotypes is associated with the community’s ratings. We find that male over-representation and traditional gender stereotypes (e.g., dominant men and submissive women) are common throughout nearly every genre in our corpus. However, only some of these stereotypes, like sexual or violent men, are associated with highly rated stories. Finally, despite women often being the target of negative stereotypes, female authors are equally likely to write such stereotypes as men.
Survey on Opinion Mining and Summarization of User Reviews on Web

Large amount of user generated data is present on web as blogs, reviews tweets, comments etc. This data involve user’s opinion, view, attitude, sentiment towards particular product, topic, event, news etc. Opinion mining (sentiment analysis) is a process of finding users’ opinion from user-generated content. Opinion summarization is useful in feedback analysis, business decision making and recommendation systems. In recent years opinion mining is one of the popular topics in Text mining and Natural Language Processing. This paper presents the methods for opinion extraction, classification, and summarization. This paper also explains different approaches, methods and techniques used in process of opinion mining and summarization, and comparative study of these different methods.  Natural Language Processing, Opinion Mining, Opinion Summarization.
Eye-Wearable Technology for Machine Maintenance: Effects of Display Position and Hands-free Operation

Exciting developments in eye-wearable technology and its potential industrial applications warrant a thorough understanding of its advantages and drawbacks through empirical evidence. We conducted an experiment to investigate what characteristics of eye-wearable technology impact user performance in machine maintenance, which included a representative set of car maintenance tasks involving Locate, Manipulate, and Compare actions. Participants were asked to follow instructions displayed on one of four technologies: a peripheral eye-wearable display, a central eye-wearable display, a tablet, or a paper manual. We found a significant effect of display position: the peripheral eye-wearable display resulted in longer completion time than the central display; but no effect for hands-free operation. The technology effects were also modulated by different Tasks and Action types. We discuss the human factors implications for designing more effective eye-wearable technology, including display position, issues of monocular display, and how the physical proximity of the technology affects users' reliance level.