nils-herrmann/clean_scirepeval huggingface dataset:
Free-Standing Leaping Experiments with a Power-Autonomous , Elastic-Spined Quadruped

We document initial experiments with Canid, a freestanding, power-autonomous quadrupedal robot equipped with a parallel actuated elastic spine. Research into robotic bounding and galloping platforms holds scientific and engineering interest because it can both probe biological hypotheses regarding bounding and galloping mammals and also provide the engineering community with a new class of agile, efficient and rapidly-locomoting legged robots. We detail the design features of Canid that promote our goals of agile operation in a relatively cheap, conventionally prototyped, commercial off-the-shelf actuated platform. We introduce new measurement methodology aimed at capturing our robot’s “body energy” during real time operation as a means of quantifying its potential for agile behavior. Finally, we present joint motor, inertial and motion capture data taken from Canid’s initial leaps into highly energetic regimes exhibiting large accelerations that illustrate the use of this measure and suggest its future potential as a platform for developing efficient, stable, hence useful bounding gaits. For more information: Kod*Lab Disciplines Electrical and Computer Engineering | Engineering | Systems Engineering Comments BibTeX entry @article{canid_spie_2013, author = {Pusey, Jason L. and Duperret, Jeffrey M. and Haynes, G. Clark and Knopf, Ryan and Koditschek , Daniel E.}, title = {Free-Standing Leaping Experiments with a PowerAutonomous, Elastic-Spined Quadruped}, pages = {87410W-87410W-15}, year = {2013}, doi = {10.1117/ 12.2016073} } This work is supported by the National Science Foundation Graduate Research Fellowship under Grant Number DGE-0822, and by the Army Research Laboratory under Cooperative Agreement Number W911NF-10–2−0016.  One print or electronic copy may be made for personal use only. Systematic or multiple reproduction, distribution to multiple locations via electronic or other means, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited. This conference paper is available at ScholarlyCommons: http://repository.upenn.edu/ese_papers/655 Free-Standing Leaping Experiments with a Power-Autonomous, Elastic-Spined Quadruped Jason L. Pusey a , Jeffrey M. Duperret b , G. Clark Haynes c , Ryan Knopf b , and Daniel E. Koditschek b a U.S. Army Research Laboratory, Aberdeen Proving Ground, MD, b University of Pennsylvania, Philadelphia, PA, c National Robotics Engineering Center, Carnegie Mellon University, Pittsburgh, PA
Generalized Feedback Shift Register Pseudorandom Number Algorithm

The generalized feedback shift register pseudorandom number algorithm has several advantages over all other pseudorandom number generators. These advantages are: (1) it produces multidimensional pseudorandom numbers; (2) it has an arbitrarily long period independent of the word size of the computer on which it is implemented; (3) it is faster than other pseudorandom number generators; (4) the “same” floating-point pseudorandom number sequence is obtained on any machine, that is, the high order mantissa bits of each pseudorandom number agree on all machines— examples are given for IBM 360, Sperry-Rand-Univac 1108, Control Data 6000, and Hewlett-Packard 2100 series computers; (5) it can be coded in compiler languages (it is portable); (6) the algorithm is easily implemented in microcode and has been programmed for an Interdata computer.
Social Computing: An Overview

A collection of technologies termed social computing is driving a dramatic evolution of the Web, matching the dot-com era in growth, excitement, and investment. All of these share a high degree of community formation, user level content creation, and a variety of other characteristics. We provide an overview of social computing and identify salient characteristics. We argue that social computing holds tremendous disruptive potential in the business world and can significantly impact society, and outline possible changes in organized human action that could be brought about. Social computing can also have deleterious effects associated with it, including security issues. We suggest that social computing should be a priority for researchers and business leaders and illustrate the fundamental shifts in communication, computing, collaboration, and commerce brought about by this trend.
Dataless Text Classification with Descriptive LDA

Manually labeling documents for training a text classifier is expensive and time-consuming. Moreover, a classifier trained on labeled documents may suffer from overfitting and adaptability problems. Dataless text classification (DLTC) has been proposed as a solution to these problems, since it does not require labeled documents. Previous research in DLTC has used explicit semantic analysis of Wikipedia content to measure semantic distance between documents, which is in turn used to classify test documents based on nearest neighbours. The semantic-based DLTC method has a major drawback in that it relies on a large-scale, finely-compiled semantic knowledge base, which is difficult to obtain in many scenarios. In this paper we propose a novel kind of model, descriptive LDA (DescLDA), which performs DLTC with only category description words and unlabeled documents. In DescLDA, the LDA model is assembled with a describing device to infer Dirichlet priors from prior descriptive documents created with category description words. The Dirichlet priors are then used by LDA to induce category-aware latent topics from unlabeled documents. Experimental results with the 20Newsgroups and RCV1 datasets show that: (1) our DLTC method is more effective than the semantic-based DLTC baseline method; and (2) the accuracy of our DLTC method is very close to state-of-the-art supervised text classification methods. As neither external knowledge resources nor labeled documents are required, our DLTC method is applicable to a wider range of scenarios.
Opinion Extraction and Summarization on the Web

The Web has become an excellent source for gathering consumer opinions. There are now numerous Web sources containing such opinions, e.g., product reviews, forums, discussion groups, and blogs. Techniques are now being developed to exploit these sources to help organizations and individuals to gain such important information easily and quickly. In this paper, we first discuss several aspects of the problem in the AI context, and then present some results of our existing work published in KDD-04 and WWW-05.
Face morphing using critical point filters

Face morphing is an effect that shows a transition from one face image to another face image smoothly. It has been widely used in various fields of work, such as animation, movie production, games, and mobile applications. Two types of methods have been used to conduct face morphing. Semi automatic mapping methods, which allow users to map corresponding pixels between two face images, can produce a smooth transition of result images. Mapping the corresponding pixel between two human face images is usually not trivial. Fully automatic methods have also been proposed for morphing between two images having similar face properties, where the results depend on the similarity of the input face images. In this project, we apply a critical point filter to determine facial features for automatically mapping the correspondence of the input face images. The critical point filters can be used to extract the main features of input face images, including color, position and edge of each facial component in the input images. An energy function is also proposed for mapping the corresponding pixels between pixels of the input face images. The experimental results show that position of each face component plays a more important role than the edge and color of the face. We can summarize that, using the critical point filter, the proposed method to generate face morphing can produce a smooth image transition with our adjusted weight function.
Collaborative robots are rapidly providing major improvements in productivity, safety, programing ease, portability and cost while addressing many new applications

Collaborative robots are rapidly providing major improvements in productivity, safety, programing ease, portability and cost while addressing many new applications Richard Bloss Article information: To cite this document: Richard Bloss , (2016),"Collaborative robots are rapidly providing major improvements in productivity, safety, programing ease, portability and cost while addressing many new applications",  An International Journal, Vol. 43 Iss 5 pp. Permanent link to this document: http://dx.doi.org/10.1108/IR-05-2016-0148
Content-based organization and visualization of music archives

With Islands of Music we present a system which facilitates exploration of music libraries without requiring manual genre classification. Given pieces of music in raw audio format we estimate their perceived sound similarities based on psychoacoustic models. Subsequently, the pieces are organized on a 2-dimensional map so that similar pieces are located close to each other. A visualization using a metaphor of geographic maps provides an intuitive interface where islands resemble genres or styles of music. We demonstrate the approach using a collection of 359 pieces of music.
Simultaneous modeling of spectrum, pitch and duration in HMM-based speech synthesis

In this paper, we describe an HMM-based speech synthesis system in which spectrum, pitch and state duration are modeled simultaneously in a unified framework of HMM. In the system, pitch and state duration are modeled by multi-space probability distribution HMMs and multi-dimensional Gaussian distributions, respectively. The distributions for spectral parameter, pitch parameter and the state duration are clustered independently by using a decision-tree based context clustering technique. Synthetic speech is generated by using an speech parameter generation algorithm from HMM and a mel-cepstrum based vocoding technique. Through informal listening tests, we have confirmed that the proposed system successfully synthesizes natural-sounding speech which resembles the speaker in the training database.
Characterizing Urban Landscapes Using Geolocated Tweets

The pervasiveness of cell phones and mobile social media applications is generating vast amounts of geolocalized user-generated content. Since the addition of geotagging information, Twitter has become a valuable source for the study of human dynamics. Its analysis is shedding new light not only on understanding human behavior but also on modeling the way people live and interact in their urban environments. In this paper, we evaluate the use of geolocated tweets as a complementary source of information for urban planning applications. Our contributions are focussed in two urban planing areas: (1) a technique to automatically determine land uses in a specific urban area based on tweeting patterns, and (2) a technique to automatically identify urban points of interest as places with high activity of tweets. We apply our techniques in Manhattan (NYC) using 49 days of geolocated tweets and validate them using land use and landmark information provided by various NYC departments. Our results indicate that geolocated tweets are a powerful and dynamic data source to characterize urban environments.
Higher LSB Layer Based Audio Steganography Technique

Steganography is the act of hiding information within other information that can only be detected by its intended recipient. Alike our previous papers here we also propose an audio steganographic method that can adjust other bits in such a way so that the stego audio signal resulting from embedding data in higher LSB layer is perceptually indistinguishable from the host audio signal. The detailed discussion makes outputs more encouraging. I.  Steganography is invented for accomplishing hidden communication . Its applications have been used in country security purpose, network security purpose and perhaps terrorists . The goal of Steganography is to " hide messages inside other harmless messages in a way that does not allow any enemy to even detect that there is a second message present " . steganography is used to cover or to hidden writing . The messages are invisible to human visionary system. Modern steganography is generally deal with electronic media rather than physical objects. Various proposals have been available for protocols to hide data in channels containing pictures [5, 6, 7], video [7, 8], audio [5, 7] and even typeset text [5, 7]. The actual information is not maintained in its original format and thereby it is converted into an alternative equivalent multimedia file like image, video or audio which in turn is being hidden within another object [9].
A Self-Paced Multiple-Instance Learning Framework for Co-Saliency Detection

As an interesting and emerging topic, co-saliency detection aims at simultaneously extracting common salient objects in a group of images. Traditional co-saliency detection approaches rely heavily on human knowledge for designing hand-crafted metrics to explore the intrinsic patterns underlying co-salient objects. Such strategies, however, always suffer from poor generalization capability to flexibly adapt various scenarios in real applications, especially due to their lack of insightful understanding of the biological mechanisms of human visual co-attention. To alleviate this problem, we propose a novel framework for this task, by naturally reformulating it as a multiple-instance learning (MIL) problem and further integrating it into a self-paced learning (SPL) regime. The proposed framework on one hand is capable of fitting insightful metric measurements and discovering common patterns under co-salient regions in a self-learning way by MIL, and on the other hand tends to promise the learning reliability and stability by simulating the human learning process through SPL. Experiments on benchmark datasets have demonstrated the effectiveness of the proposed framework as compared with the state-of-the-arts.
Non-invasive wearable electrochemical sensors: a review.

Wearable sensors have garnered considerable recent interest owing to their tremendous promise for a plethora of applications. Yet the absence of reliable non-invasive chemical sensors has greatly hindered progress in the area of on-body sensing. Electrochemical sensors offer considerable promise as wearable chemical sensors that are suitable for diverse applications owing to their high performance, inherent miniaturization, and low cost. A wide range of wearable electrochemical sensors and biosensors has been developed for real-time non-invasive monitoring of electrolytes and metabolites in sweat, tears, or saliva as indicators of a wearer's health status. With continued innovation and attention to key challenges, such non-invasive electrochemical sensors and biosensors are expected to open up new exciting avenues in the field of wearable wireless sensing devices and body-sensor networks, and thus find considerable use in a wide range of personal health-care monitoring applications, as well as in sport and military applications.
Identifying Argumentative Discourse Structures in Persuasive Essays

In this paper, we present a novel approach for identifying argumentative discourse structures in persuasive essays. The structure of argumentation consists of several components (i.e. claims and premises) that are connected with argumentative relations. We consider this task in two consecutive steps. First, we identify the components of arguments using multiclass classification. Second, we classify a pair of argument components as either support or non-support for identifying the structure of argumentative discourse. For both tasks, we evaluate several classifiers and propose novel feature sets including structural, lexical, syntactic and contextual features. In our experiments, we obtain a macro F1-score of 0.726 for identifying argument components and 0.722 for argumentative relations.
Splash: Fast Data Dissemination with Constructive Interference in Wireless Sensor Networks

It is well-known that the time taken for disseminating a large data object over a wireless sensor network is dominated by the overhead of resolving the contention for the underlying wireless channel. In this paper, we propose a new dissemination protocol called Splash, that eliminates the need for contention resolution by exploiting constructive interference and channel diversity to effectively create fast and parallel pipelines over multiple paths that cover all the nodes in a network. We call this tree pipelining. In order to ensure high reliability, Splash also incorporates several techniques, including exploiting transmission density diversity, opportunistic overhearing, channel-cycling and XOR coding. Our evaluation results on two large-scale testbeds show that Splash is more than an order of magnitude faster than state-ofthe-art dissemination protocols and achieves a reduction in data dissemination time by a factor of more than 20 compared to DelugeT2.
Active filter design using operational transconductance amplifiers: A tutorial

Basic properties of the operational transconductance amplifier (OTA) are discussed. Applications of the OTA in voltage-controlled amplifiers, filters, and impedances are presented. A versatile family of voltage-controlled filter sections suitable for systematic design requirements is described. The total number of components used in these circuits is small, and the design equations and voltage-control characteristics are attractive. Limitations as well as practical considerations of OTA-based filters using commercially available bipolar OTAs are discussed. Applications of OTAs in continuous-time monolithic filters are considered.
Managing Online Trolling: From Deviant to Social and Political Trolls

Trolling behaviors are extremely diverse, varying by context, tactics, motivations, and impact. Definitions, perceptions of, and reactions to online trolling behaviors vary. Since not all trolling is equal or deviant, managing these behaviors requires context sensitive strategies. This paper describes appropriate responses to various acts of trolling in context, based on perceptions of college students in North America. In addition to strategies for dealing with deviant trolling, this paper illustrates the complexity of dealing with socially and politically motivated trolling.
A Smart Parking System based on IoT protocols and emerging enabling technologies

Enabling a sustainable mobility is one of primary goals of the so-called Smart City vision, and the development of intelligent parking systems represents a key aspect. To this purpose, this paper presents a novel Smart Parking System based on the jointly use of different technologies, such as RFID, WSN, NFC, Cloud, and mobile. It is able to collect, in real time, both environmental parameters and information about the occupancy state of parking spaces, and to direct drivers to the nearest vacant parking spot by using a customized software application. This last one leverages a NFC-based e-wallet system to allow users to pay for parking fees. Furthermore, a customized software application, installed on a cloud platform, is able to manage alert events (e.g. improper use of a reserved space or expiration of the purchased time). In such a case, it promptly informs the traffic cops through an Android mobile app, which has been designed ad hoc for the considered scenario.
Fast: A Transducer-Based Language for Tree Manipulation

We introduce a tree manipulation language, Fast, that overcomes technical limitations of previous tree manipulation languages, such as XPath and XSLT which do not support precise program analysis, or TTT and Tiburon which only support trees over finite alphabets. At the heart of Fast is a combination of SMT solvers and tree transducers, enabling it to model programs whose input and output can range over any decidable theory. The language can express multiple applications. We write an HTML “sanitizer” in Fast and obtain results comparable to leading libraries but with smaller code. Next we show how augmented reality “tagging” applications can be checked for potential overlap in milliseconds using Fast type checking. We show how transducer composition enables deforestation for improved performance. Overall, we strike a balance between expressiveness and precise analysis that works for a large class of important tree-manipulating programs.
Mechanical design and motion control of a biomimetic robotic dolphin

This paper addresses the design, construction and control issues of a novel biomimetic robotic dolphin equipped with mechanical flippers, based on an engineered propulsive model. The robotic dolphin is modeled as a three-segment organism composed of a rigid anterior body, a flexible rear body and an oscillating fluke. The dorsoventral movement of the tail produces the thrust and bending of the anterior body in the horizontal plane enables turning maneuvers. A dualmicrocontroller structure is adopted to drive the oscillating multi-link rear body and the mechanical flippers. Experimental results primarily confirm the effectiveness of the dolphin-like movement in propulsion and maneuvering.
Learning Distributed Representations of Concepts Using Linear Relational Embedding

In this paper we introduce Linear Relational Embedding as a means of learning a distributed representation of concepts from data consisting of binary relations between concepts. The key idea is to represent concepts as vectors, binary relations as matrices, and the operation of applying a relation to a concept as a matrix-vector multiplication that produces an approximation to the related concept. A representation for concepts and relations is learned by maximizing an appropriate discriminative goodness function using gradient ascent. On a task involving family relationships, learning is fast and leads to good generalization. Learning Distributed Representations of Concepts using Linear Relational Embedding Alberto Paccanaro Geoffrey Hinton Gatsby Unit
MISSL: multiple-instance semi-supervised learning

There has been much work on applying multiple-instance (MI) learning to content-based image retrieval (CBIR) where the goal is to rank all images in a known repository using a small labeled data set. Most existing MI learning algorithms are non-transductive in that the images in the repository serve only as test data and are not used in the learning process. We present MISSL (Multiple-Instance Semi-Supervised Learning) that transforms any MI problem into an input for a graph-based single-instance semi-supervised learning method that encodes the MI aspects of the problem simultaneously working at both the bag and point levels. Unlike most prior MI learning algorithms, MISSL makes use of the unlabeled data.
Towards IEEE 802.1 Ethernet AVB for Advanced Driver Assistance Systems: A preliminary assessment

Advanced Driver Assistance Systems involving cameras and the multimedia domain are both quite challenging for automotive network technologies, due to the bandwidth requirements and latency/jitter constraints they impose. Ethernet as in-car network is expected to breakthrough in both domains and the IEEE Audio Video Bridging (AVB) standard is a promising candidate. This paper reports preliminary performance assessments of AVB for ADAS, multimedia and infotainment, obtained through the simulation of realistic traffic patterns.