nils-herrmann/clean_scirepeval huggingface dataset:
Model-Driven Data Acquisition in Sensor Networks

Declarative queries are proving to be an attractive paradigm for interacting with networks of wireless sensors. The metaphor that “the sensornet is a database” is problematic, however, because sensors do not exhaustively represent the data in the real world. In order to map the raw sensor readings onto physical reality, a modelof that reality is required to complement the readings. In this paper, we enrich interactive sensor querying with statistical modeling techniques. We demonstrate that such models can help provide answers that are both more meaningful, and, by introducing approximations with probabilistic confidences, significantly more efficient to compute in both time and energy. Utilizing the combination of a model and live data acquisition raises the challenging optimization problem of selecting the best sensor readings to acquire, balancing the increase in the confidence of our answer against the communication and data acquisition costs in the network. We describe an exponential time algorithm for finding the optimal solution to this optimization problem, and a polynomial-time heuristic for identifying solutions that perform well in practice. We evaluate our approach on several real-world sensor-network data sets, taking into account the real measured data and communication quality, demonstrating that our model-based approach provides a high-fidelity representation of the real phenomena and leads to significant performance gains versus traditional data acquisition techniques.
Measuring effects of music, noise, and healing energy using a seed germination bioassay.

To measure biologic effects of music, noise, and healing energy without human preferences or placebo effects using seed germination as an objective biomarker.



A series of five experiments were performed utilizing okra and zucchini seeds germinated in acoustically shielded, thermally insulated, dark, humid growth chambers. Conditions compared were an untreated control, musical sound, pink noise, and healing energy. Healing energy was administered for 15-20 minutes every 12 hours with the intention that the treated seeds would germinate faster than the untreated seeds. The objective marker was the number of seeds sprouted out of groups of 25 seeds counted at 12-hour intervals over a 72-hour growing period. Temperature and relative humidity were monitored every 15 minutes inside the seed germination containers. A total of 14 trials were run testing a total of 4600 seeds.



Musical sound had a highly statistically significant effect on the number of seeds sprouted compared to the untreated control over all five experiments for the main condition (p < 0.002) and over time (p < 0.000002). This effect was independent of temperature, seed type, position in room, specific petri dish, and person doing the scoring. Musical sound had a significant effect compared to noise and an untreated control as a function of time (p < 0.03) while there was no significant difference between seeds exposed to noise and an untreated control. Healing energy also had a significant effect compared to an untreated control (main condition, p < 0.0006) and over time (p < 0.0001) with a magnitude of effect comparable to that of musical sound.



This study suggests that sound vibrations (music and noise) as well as biofields (bioelectromagnetic and healing intention) both directly affect living biologic systems, and that a seed germination bioassay has the sensitivity to enable detection of effects caused by various applied energetic conditions.
Transfer metric learning for action similarity using high-level semantics

The goal of transfer learning is to exploit previous experiences and knowledge in order to improve learning in a novel domain. This is especially beneficial for the challenging task of learning classifiers that generalize well when only few training examples are available. In such a case, knowledge transfer methods can help to compensate for the lack of data. The performance and robustness against negative transfer of these approaches is influenced by the interdependence between knowledge representation and transfer type. However, this important point is usually neglected in the literature; instead the focus lies on either of the two aspects. In contrast, we study in this work the effect of various high-level semantic knowledge representations on different transfer types in a novel generic transfer metric learning framework. Furthermore, we introduce a hierarchical knowledge representation model based on the embedded structure in the semantic attribute space. The evaluation of the framework on challenging transfer settings in the context of action similarity demonstrates the effectiveness of our approach compared to state-of-the-art.
The Impact of Service Quality on Customer Satisfaction , Customer Loyalty and Brand Image : Evidence from Hotel Industry of Pakistan

This study intent to investigate the impact of service quality on consumer satisfaction customer loyalty and brand image. The primary data was collected from 5 and 8 star hotels of Pakistan. The response rate was 86%. Structural equation modeling (SEM) technique was used to analyze the data. The findings suggested that high quality of services boost up the customer satisfaction and then afterward this satisfaction will strengthen the customer loyalty, our results also matched with Brodie et al., (2009). Last but not the least strong customer loyalty directly related to strong brand image.
A fourth order CMOS band pass filter for PIR sensors

This paper describes a fourth order continuous time band pass filter used to filter analog signals coming from passive infrared sensors (PIR). Specifications for this filter are characterized by an extremely large time constant which strongly condition the topology of the filter used. Moreover, very low power consumption is also required, since these filters are implemented together with the sensor in wireless sensor motes for ambient intelligence applications where power dissipation is a key factor. In this paper a low power and low frequency fourth order CMOS band pass filtering for PIR signal conditioning is presented. Results obtained from simulations of this circuit show the initial validity of the proposed approach.
A Robot that Approaches Pedestrians

When robots serve in urban areas such as shopping malls, they will often be required to approach people in order to initiate service. This paper presents a technique for human–robot interaction that enables a robot to approach people who are passing through an environment. For successful approach, our proposed planner first searches for a target person at public distance zones anticipating his/her future position and behavior. It chooses a person who does not seem busy and can be reached from a frontal direction. Once the robot successfully approaches the person within the social distance zone, it identifies the person's reaction and provides a timely response by coordinating its body orientation. The system was tested in a shopping mall and compared with a simple approaching method. The result demonstrates a significant improvement in approaching performance; the simple method was only 35.1% successful, whereas the proposed technique showed a success rate of 55.9%.
SuperParsing: Scalable Nonparametric Image Parsing with Superpixels

This paper presents a simple and effective nonparametric approach to the problem of image parsing, or labeling image regions (in our case, superpixels produced by bottom-up segmentation) with their categories. This approach requires no training, and it can easily scale to datasets with tens of thousands of images and hundreds of labels. It works by scene-level matching with global image descriptors, followed by superpixel-level matching with local features and efficient Markov random field (MRF) optimization for incorporating neighborhood context. Our MRF setup can also compute a simultaneous labeling of image regions into semantic classes (e.g., tree, building, car) and geometric classes (sky, vertical, ground). Our system outperforms the state-of-the-art nonparametric method based on SIFT Flow on a dataset of 2,688 images and 33 labels. In addition, we report per-pixel rates on a larger dataset of 15,150 images and 170 labels. To our knowledge, this is the first complete evaluation of image parsing on a dataset of this size, and it establishes a new benchmark for the problem.
SoftGUESS: Visualization and Exploration of Code Clones in Context

We introduce SoftGUESS, a code clone exploration system. SoftGUESS is built on the more general GUESS system which provides users with a mechanism to interactively explore graph structures both through direct manipulation as well as a domain-specific language. We demonstrate SoftGUESS through a number of mini-applications to analyze evolutionary code-clone behavior in software systems. The mini-applications of SoftGUESS represent a novel way of looking at code-clones in the context of many system features. It is our hope that SoftGUESS will form the basis for other analysis tools in the software-engineering domain.
A New Actuator System Using Dual-Motors and a Planetary Gear

This paper proposes a new actuator system for mobile robot applications. Since the actuator system to be proposed consists of dual-motors and a planetary gear, it is referred to as the dual-motor system using a planetary gear (DuPG) in this paper. The suggested DuPG improves the speed-torque performance by combining two motors with one planetary gear, in which one motor is for high speed and the other for high torque. In other words, it is able to realize a high speed at low torque, and high torque at low speed, as an automatic transmission operates in an automobile. The proposed actuator system is able to extend the speed/torque operation region. Finally, several experimental results are suggested to show a few advantages such as the extended speed/torque operation region and the enhanced energy efficiency.
Lévy flights and random searches

In this work we discuss some recent contributions to the random search problem. Our analysis includes superdiffusive Lévy processes and correlated random walks in several regimes of target site density, mobility and revisitability. We present results in the context of mean-field-like and closed-form average calculations, as well as numerical simulations. We then consider random searches performed in regular lattices and lattices with defects, and we discuss a necessary criterion for distinguishing true superdiffusion from correlated random walk processes. We invoke energy considerations in relation to critical survival states on the edge of extinction, and we analyze the emergence of Lévy behavior in deterministic search walks. Finally, we comment on the random search problem in the context of biological foraging. PACS numbers: 05.50.Fb, 05.40.−a
Integrating real-time and batch processing in a polystore

This paper describes a stream processing engine called S-Store and its role in the BigDAWG polystore. Fundamentally, S-Store acts as a frontend processor that accepts input from multiple sources, and massages it into a form that has eliminated errors (data cleaning) and translates that input into a form that can be efficiently ingested into BigDAWG. S-Store also acts as an intelligent router that sends input tuples to the appropriate components of BigDAWG. All updates to S-Store's shared memory are done in a transactionally consistent (ACID) way, thereby eliminating new errors caused by non-synchronized reads and writes. The ability to migrate data from component to component of BigDAWG is crucial. We have described a migrator from S-Store to Postgres that we have implemented as a first proof of concept. We report some interesting results using this migrator that impact the evaluation of query plans.
Websites that Satisfy Users: A Theoretical Framework for Web User Interface Design and Evaluation

With the fast development and increasing use of the World Wide Web as both an information seeking and an electronic commerce tool, web usability studies grow in importance. While web designers have largely focused on functional aspects of websites, there has been little systematic attention to (1) the motivational issues of web user interface design or (2) a theoretically driven approach to web user satisfaction studies. The objective of this paper is to provide a conceptual framework and foundation for systematically investigating features in the web environment that contribute to user satisfaction with a web interface. This research uses Herzberg’s  motivation-hygiene theory to guide the identification of these features. Among the implications and contributions of this research are the identification of web design features that may maximize the likelihood of user satisfaction and return visits to the web site.
Separating the real from the synthetic: minutiae histograms as fingerprints of fingerprints

In this study we show that by the current state-of-the-art synthetically generated fingerprints can easily be discriminated from real fingerprints. We propose a method based on second order extended minutiae histograms (MHs) which can distinguish between real and synthetic prints with very high accuracy. MHs provide a fixed-length feature vector for a fingerprint which are invariant under rotation and translation. This ’test of realness’ can be applied to synthetic fingerprints produced by any method. In this work, tests are conducted on the 12 publicly available databases of FVC2000, FVC2002 and FVC2004 which are well established benchmarks for evaluating the performance of fingerprint recognition algorithms; 3 of these 12 databases consist of artificial fingerprints generated by the SFinGe software. Additionally, we evaluate the discriminative performance on a database of synthetic fingerprints generated by the software of Bicz versus real fingerprint images. We conclude with suggestions for the improvement of synthetic fingerprint generation.
Automatic Game Design via Mechanic Generation

Game designs often center on the game mechanics— rules governing the logical evolution of the game. We seek to develop an intelligent system that generates computer games. As first steps towards this goal we present a composable and cross-domain representation for game mechanics that draws from AI planning action representations. We use a constraint solver to generate mechanics subject to design requirements on the form of those mechanics—what they do in the game. A planner takes a set of generated mechanics and tests whether those mechanics meet playability requirements—controlling how mechanics function in a game to affect player behavior. We demonstrate our system by modeling and generating mechanics in a role-playing game, platformer game, and combined role-playing-platformer game.
Inverse Reinforcement Learning in Relational Domains

In this work, we introduce the first approach to the Inverse Reinforcement Learning (IRL) problem in relational domains. IRL has been used to recover a more compact representation of the expert policy leading to better generalization performances among different contexts. On the other hand, relational learning allows representing problems with a varying number of objects (potentially infinite), thus provides more generalizable representations of problems and skills. We show how these different formalisms allow one to create a new IRL algorithm for relational domains that can recover with great efficiency rewards from expert data that have strong generalization and transfer properties. We evaluate our algorithm in representative tasks and study the impact of diverse experimental conditions such as : the number of demonstrations, knowledge about the dynamics, transfer among varying dimensions of a problem, and changing dynamics.
Basic considerations and topologies of switched-mode assisted linear power amplifiers

The paper presents a combined power amplifier system consisting of a linear amplifier unit with a switched-mode (class D) current dumping stage arranged in parallel. With this topology the fundamental drawback of conventional linear power amplifiers the high loss is avoided. Compared to a pure class D (switching) amplifier the presented system needs no output filter to reduce the switching frequency harmonics. This filter (usually of multi-stage type) generally deteriorates the transient response of the system and impairs the feedback loop design. Furthermore, the low-frequency distortions of switching amplifiers caused by the interlock delay of their power transistors are avoided with the presented switched-mode assisted linear amplifier system. This can be considered as a master-slave system with a guiding linear amplifier and a supporting class D slave unit. The paper describes the operating principle of the system, analyzes the fundamental relationships for the circuit design and presents simulation results. Finally, various further topologies of switched-mode assisted linear amplifiers are given.
Challenges in measuring online advertising systems

Online advertising supports many Internet services, such as search, email, and social networks. At the same time, there are widespread concerns about the privacy loss associated with user targeting. Yet, very little is publicly known about how ad networks operate, especially with regard to how they use user information to target users. This paper takes a first principled look at measurement methodologies for ad networks. It proposes new metrics that are robust to the high levels of noise inherent in ad distribution, identifies measurement pitfalls and artifacts, and provides mitigation strategies. It also presents an analysis of how three different classes of advertising -- search, contextual, and social networks, use user profile information today.
LaBB-CAT: an Annotation Store

“ONZE Miner”, an open-source tool for storing and automatically annotating Transcriber transcripts, has been redeveloped to use “annotation graphs” as its data model. The annotation graph framework provides the new software, “LaBB-CAT”, greater flexibility for automatic and manual annotation of corpus data at various independent levels of granularity, and allows more sophisticated annotation structures, opening up new possibilities for corpus mining and conversion between tool formats.
Building Structured Databases of Factual Knowledge from Massive Text Corpora

In today's computerized and information-based society, people are inundated with vast amounts of text data, ranging from news articles, social media post, scientific publications, to a wide range of textual information from various domains (corporate reports, advertisements, legal acts, medical reports). To turn such massive unstructured text data into structured, actionable knowledge, one of the grand challenges is to gain an understanding of the factual information (e.g., entities, attributes, relations) in the text.
 In this tutorial, we introduce data-driven methods on mining structured facts (i.e., entities and their relations/attributes for types of interest) from massive text corpora, to construct structured databases of factual knowledge (called StructDBs). State-of-the-art information extraction systems have strong reliance on large amounts of task/corpus-specific labeled data (usually created by domain experts). In practice, the scale and efficiency of such a manual annotation process are rather limited, especially when dealing with text corpora of various kinds (domains, languages, genres). We focus on methods that are minimally-supervised, domain-independent, and language-independent for timely StructDB construction across various application domains (news, social media, biomedical, business), and demonstrate on real datasets how these StructDBs aid in data exploration and knowledge discovery.
Learning to Predict Readability using Diverse Linguistic Features

In this paper we consider the problem of building a system to predict readability of natural-language documents. Our system is trained using diverse features based on syntax and language models which are generally indicative of readability. The experimental results on a dataset of documents from a mix of genres show that the predictions of the learned system are more accurate than the predictions of naive human judges when compared against the predictions of linguistically-trained expert human judges. The experiments also compare the performances of different learning algorithms and different types of feature sets when used for predicting readability.
The mathematical theory of communication

T HE recent development of various methods of modulation such as PCM and PPM which exchange bandwidth for signal-to-noise ratio has intensified the interest in a general theory of communication. A basis for such a theory is contained in the important papers of Nyquist 1 and Hartley2 on this subject. In the present paper we will extend the theory to include a number of new factors, in particular the effect of noise in the channel, and the savings possible due to the statistical structure of the original message and due to the nature of the final destination of the information. The fundamental problem of communication is that of reproducing at one point either exactly or approximately a message selected at another point. Frequently the messages have meaning; that is they refer to or are correlated according to some system with certain physical or conceptual entities. These semantic aspects of communication are irrelevant to the engineering problem. The significant aspect is that the actual message is one selected from a set of possible messages. The system must be designed to operate for each possible selection, not just the one which will actually be chosen since this is unknown at the time of design. If the number of messages in the set is finite then this number or any monotonic function of this number can be regarded as a measure of the information produced when one message is chosen from the set, all choices being equally likely. As was pointed out by Hartley the most natural choice is the logarithmic function. Although this definition must be generalized considerably when we consider the influence of the statistics of the message and when we have a continuous range of messages, we will in all cases use an essentially logarithmic measure. The logarithmic measure is more convenient for various reasons: