nils-herrmann/clean_scirepeval huggingface dataset:
Characterization of silicone rubber based soft pneumatic actuators

Conventional pneumatic actuators have been a popular choice due to their decent force/torque output. Nowadays, new generation of pneumatic actuator made out of highly compliant elastomers, which we call soft pneumatic actuators (SPA), are drawing increasing attention due to their ease of fabrication, high customizability and innately softness. However, there is no effective method presented to characterize and understand these actuators, such as to measure the force and torque output, range of motion and the speed of actuation. In this work, we present two types of SPAs: bending and rotary actuators. In addition, we have developed two measurement setups to characterize actuators of different geometries. The measured force/torque outputs of different actuators are presented and analyzed. Step responses to certain pressure input are presented and discussed. A simple model is presented to provide physical insight to the observed behavior of the soft actuators. This work provides the basis for designing customized SPAs with application-specific requirements.
Convergence Problems with Generative Adversarial Networks (GANs)

Generative adversarial networks (GANs) are a novel approach to generative modelling, a task whose goal it is to learn a distribution of real data points. They have often proved difficult to train: GANs are unlike many techniques in machine learning, in that they are best described as a two-player game between a discriminator and generator. This has yielded both unreliability in the training process, and a general lack of understanding as to how GANs converge, and if so, to what. The purpose of this dissertation is to provide an account of the theory of GANs suitable for the mathematician, highlighting both positive and negative results. This involves identifying the problems when training GANs, and how topological and game-theoretic perspectives of GANs have contributed to our understanding and improved our techniques in recent years.
Domestic food and sustainable design: a study of university student cooking and its impacts

In four university student kitchens over twenty-one days, we captured participants' food preparation activity, quantified the greenhouse gas emissions and direct energy connected to the food and cooking, and talked to participants about their food practices. Grounded in this uniquely detailed micro-account, our findings inform sustainable design for cooking and eating at home and quantify the potential impacts. We outline the relation of the impacts to our participants' approaches to everyday food preparation, the organisation of their time, and the role of social meals. Our technique allows evaluation of opportunities for sustainable intervention design: at the appliance, in the digitally-mediated organisation of meals and inventory management, and more broadly in reflecting upon and reshaping diet.
Randomized Rumor Spreading

We investigate the class of so-called epidemic algorithms that are commonly used for the lazy transmission of updates to distributed copies of a database. These algorithms use a simple randomized communication mechanism to ensure robustness. Suppose players communicate in parallel rounds in each of which every player calls a randomly selected communication partner. In every round, players can generate rumors (updates) that are to be distributed among all players. Whenever communication is established between two players, each one must decide which of the rumors to transmit. The major problem (arising due to the randomization) is that players might not know which rumors their partners have already received. For example, a standard algorithm forwarding each rumor from the calling to the called players for rounds needs to transmit the rumor times in order to ensure that every player finally receives the rumor with high probability. We investigate whether such a large communication overhead is inherent to epidemic algorithms. On the positive side, we show that the communication overhead can be reduced significantly. We give an algorithm using only transmissions and rounds. In addition, we prove the robustness of this algorithm, e.g., against adversarial failures. On the negative side, we show that any address-oblivious algorithm (i.e., an algorithm that does not use the addresses of communication partners) needs to send messages for each rumor regardless of the number of rounds. Furthermore, we give a general lower bound showing that timeand communicationoptimality cannot be achieved simultaneously using random phone calls, that is, every algorithm that distributes a rumor Email: karp@icsi.berkeley.edu. International Computer Science Institute, Berkeley and University of California at Berkeley.  schindel@tcs.mu-luebeck.de, Institut für Theoretische Informatik, Med. Universität zu Lübeck. Parts of this work are supported by a stipend of the “Gemeinsames Hochschulsonderprogramm III von Bund und Länder” through the DAAD.   voecking@mpi-sb.mpg.de. Max-Planck-Institut für Informatik, Saarbrücken, Germany. in rounds needs transmissions.
EasyLiving: Technologies for Intelligent Environments

The EasyLiving project is concerned with development of an architecture and technologies for intelligent environments which allow the dynamic aggregation of diverse I/O devices into a single coherent user experience. Components of such a system include middleware (to facilitate distributed computing), world modelling (to provide location-based context), perception (to collect information about world state), and service description (to support decomposition of device control, internal logic, and user interface). This paper describes the current research in each of these areas, highlighting some common requirements for any intelligent environment.
5G uplink performance of filter bank multi-carrier

One of 5G communication typical scenarios is machine-to-machine communication with large number of devices, which proposes new requirements for uplink transmission. Filter bank multi-carrier with offset quadrature amplitude modulation (FBMC/OQAM) is one of the popular nonorthogonal waveform candidates for 5G transmission. In this paper, FBMC/OQAM and cyclic prefix OFDM with single antenna settings are comprehensively evaluated and compared in uplink transmission form four aspects: power spectral density, timing and frequency misalignments, guard bands between adjacent users and time efficiency. At last, our comparison results show that FBMC is more suitable for 5G uplink multi-user asynchronous transmissions than CP-OFDM.
A 64-Element 28-GHz Phased-Array Transceiver With 52-dBm EIRP and 8–12-Gb/s 5G Link at 300 Meters Without Any Calibration

This paper presents a 64-element 28-GHz phased-array transceiver for 5G communications based on <inline-formula> <tex-math notation="LaTeX">$2\times 2$ </tex-math></inline-formula> transmit/ receive (TRX) beamformer chips. Sixteen of the <inline-formula> <tex-math notation="LaTeX">$2\times 2$ </tex-math></inline-formula> TRX chips are assembled on a 12-layer printed circuit board (PCB) together with a Wilkinson combiner/divider network and 28–32-GHz stacked-patch antennas. The 64-element array results in 1.1 dB and 8.9° rms amplitude and phase error, respectively, with no calibration due to the symmetric design of the <inline-formula> <tex-math notation="LaTeX">$2\times 2$ </tex-math></inline-formula> beamformer chips and the PCB Wilkinson network. The effect of phase and amplitude mismatch between the 64 elements is analyzed and shown to have little impact on the 64-element array performance due to the averaging effects of phased arrays. Detailed pattern, effective isotropic radiated power (EIRP), and link measurements performed without any array calibration are presented and show the robustness of the symmetrical design technique. The phased array can scan to ±50° in azimuth (<inline-formula> <tex-math notation="LaTeX">$H$ </tex-math></inline-formula>-plane) and ±25° in elevation (<inline-formula> <tex-math notation="LaTeX">$E$ </tex-math></inline-formula>-plane) with low sidelobes and achieves a saturated EIRP of 52 dBm with 4-GHz 3-dB bandwidth. A 300-m wireless link is demonstrated with a record-setting data rate of 8–12 Gb/s over all scan angles using two 64-element TRX arrays and 16-/64-QAM waveforms.
CrowdProbe: Non-invasive Crowd Monitoring with Wi-Fi Probe

Devices with integrated Wi-Fi chips broadcast beacons for network connection management purposes. Such information can be captured with inexpensive monitors and used to extract user behavior. To understand the behavior of visitors, we deployed our passive monitoring system---CrowdProbe, in a multi-floor museum for six months. We used a Hidden Markov Models (HMM) based trajectory inference algorithm to infer crowd movement using more than 1.7 million opportunistically obtained probe request frames.
 However, as more devices adopt schemes to randomize their MAC addresses in the passive probe session to protect user privacy, it becomes more difficult to track crowd and understand their behavior. In this paper, we try to make use of historical transition probability to reason about the movement of those randomized devices with spatial and temporal constraints. With CrowdProbe, we are able to achieve sufficient accuracy to understand the movement of visitors carrying devices with randomized MAC addresses.
Discrete and Continuous, Probabilistic Anticipation for Autonomous Robots in Urban Environments

This paper develops a probabilistic anticipation algorithm for dynamic objects observed by an autonomous robot in an urban environment. Predictive Gaussian mixture models are used due to their ability to probabilistically capture continuous and discrete obstacle decisions and behaviors; the predictive system uses the probabilistic output (state estimate and covariance) of a tracking system and map of the environment to compute the probability distribution over future obstacle states for a specified anticipation horizon. A Gaussian splitting method is proposed based on the sigma-point transform and the nonlinear dynamics function, which enables increased accuracy as the number of mixands grows. An approach to caching elements of this optimal splitting method is proposed, in order to enable real-time implementation. Simulation results and evaluations on data from the research community demonstrate that the proposed algorithm can accurately anticipate the probability distributions over future states of nonlinear systems.
A Parallel Algorithm for Enumerating All Maximal Cliques in Complex Network

Efficient enumeration of all maximal cliques in a given graph has many applications in graph theory, data mining and bio informatics. However, the exponentially increasing computation time of this problem confines the scale of the graph. Meanwhile, recent researches show that many networks in our world are complex networks involving massive data. To solve the maximal clique problem in the real-world scenarios, this paper presents a parallel algorithm Peamc (parallel enumeration of all maximal cliques) which exploits several new and effective techniques to enumerate all maximal cliques in a complex network. Furthermore, we provide a performance study on a true-life call graph with up to 2,423,807 vertices and 5,317,183 edges. The experimental results show that Peamc can find all the maximal cliques in a complex network with high efficiency and scalability
We know what @you #tag: does the dual role affect hashtag adoption?

Researchers and social observers have both believed that hashtags, as a new type of organizational objects of information, play a dual role in online microblogging communities (e.g., Twitter). On one hand, a hashtag serves as a bookmark of content, which links tweets with similar topics; on the other hand, a hashtag serves as the symbol of a community membership, which bridges a virtual community of users. Are the real users aware of this dual role of hashtags? Is the dual role affecting their behavior of adopting a hashtag? Is hashtag adoption predictable? We take the initiative to investigate and quantify the effects of the dual role on hashtag adoption. We propose comprehensive measures to quantify the major factors of how a user selects content tags as well as joins communities. Experiments using large scale Twitter datasets prove the effectiveness of the dual role, where both the content measures and the community measures significantly correlate to hashtag adoption on Twitter. With these measures as features, a machine learning model can effectively predict the future adoption of hashtags that a user has never used before.
Optimized Performance Evaluation of LTE Hard Handover Algorithm with Average RSRP Constraint

Hard handover mechanism is adopted to be used in 3GPP Long Term Evolution (3GPP LTE) in order to reduce the complexity of the LTE network architecture. This mechanism comes with degradation in system throughput as well as a higher system delay. This paper proposes a new handover algorithm known as LTE Hard Handover Algorithm with Average Received Signal Reference Power (RSRP) Constraint (LHHAARC) in order to minimize number of handovers and the system delay as well as maximize the system throughput. An optimized system performance of the LHHAARC is evaluated and compared with three well-known handover algorithms via computer simulation. The simulation results show that the LHHAARC outperforms three well-known handover algorithms by having less number of average handovers per UE per second, shorter total system delay whilst maintaining a higher total system throughput.
Footstep and Motion Planning in Semi-unstructured Environments Using Possibility Graphs

Traversing environments with arbitrary obstacles poses significant challenges for bipedal robots. In some cases, whole body motions may be necessary to maneuver around an obstacle, but most existing footstep planners can only select from a discrete set of predetermined footstep actions; they are unable to utilize the continuum of whole body motion that is truly available to the robot platform. Existing motion planners that can utilize whole body motion tend to struggle with the complexity of large-scale problems. We introduce a planning method, called the “Possibility Graph”, which uses high-level approximations of constraint manifolds to rapidly explore the “possibility” of actions, thereby allowing lower-level motion planners to be utilized more efficiently. We demonstrate simulations of the method working in a variety of semi-unstructured environments. In this context, “semi-unstructured” means the walkable terrain is flat and even, but there are arbitrary 3D obstacles throughout the environment which may need to be stepped over or maneuvered around using whole body motions.
A case for query by image and text content: searching computer help using screenshots and keywords

The multimedia information retrieval community has dedicated extensive research effort to the problem of content-based image retrieval (CBIR). However, these systems find their main limitation in the difficulty of creating pictorial queries. As a result, few systems offer the option of querying by visual examples, and rely on automatic concept detection and tagging techniques to provide support for searching visual content using textual queries.
 This paper proposes and studies a practical multimodal web search scenario, where CBIR fits intuitively to improve the retrieval of rich information queries. Many online articles contain useful know-how knowledge about computer applications. These articles tend to be richly illustrated by screenshots. We present a system to search for such software know-how articles that leverages the visual correspondences between screenshots. Users can naturally create pictorial queries simply by taking a screenshot of the application to retrieve a list of articles containing a matching screenshot.
 We build a prototype comprising 150k articles that are classified into walkthrough, book, gallery, and general categories, and provide a comprehensive evaluation of this system, focusing on technical (accuracy of CBIR techniques) and usability (perceived system usefulness) aspects. We also consider the study of added value features of such a visual-supported search, including the ability to perform cross-lingual queries. We find that the system is able to retrieve matching screenshots for a wide variety of programs, across language boundaries, and provide subjectively more useful results than keyword-based web and image search engines.
Information Gain-based Exploration Using Rao-Blackwellized Particle Filters

This paper presents an integrated approach to exploration, mapping, and localization. Our algorithm uses a highly efficient Rao-Blackwellized particle filter to represent the posterior about maps and poses. It applies a decision-theoretic framework which simultaneously considers the uncertainty in the map and in the pose of the vehicle to evaluate potential actions. Thereby, it trades off the cost of executing an action with the expected information gain and takes into account possible sensor measurements gathered along the path taken by the robot. We furthermore describe how to utilize the properties of the Rao-Blackwellization to efficiently compute the expected information gain. We present experimental results obtained in the real world and in simulation to demonstrate the effectiveness of our approach.
Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data

Follow up what we will offer in this article about web data mining exploring hyperlinks contents and usage data. You know really that this book is coming as the best seller book today. So, when you are really a good reader or you're fans of the author, it does will be funny if you don't have this book. It means that you have to get this book. For you who are starting to learn about something new and feel curious about this book, it's easy then. Just get this book and feel how this book will give you more exciting lessons.
Mutations in the Profilin 1 Gene Cause Familial Amyotrophic Lateral Sclerosis

Amyotrophic lateral sclerosis (ALS) is a late-onset neurodegenerative disorder resulting from motor neuron death. Approximately 10% of cases are familial (FALS), typically with a dominant inheritance mode. Despite numerous advances in recent years, nearly 50% of FALS cases have unknown genetic aetiology. Here we show that mutations within the profilin 1 (PFN1) gene can cause FALS. PFN1 is crucial for the conversion of monomeric (G)-actin to filamentous (F)-actin. Exome sequencing of two large ALS families showed different mutations within the PFN1 gene. Further sequence analysis identified 4 mutations in 7 out of 274 FALS cases. Cells expressing PFN1 mutants contain ubiquitinated, insoluble aggregates that in many cases contain the ALS-associated protein TDP-43. PFN1 mutants also display decreased bound actin levels and can inhibit axon outgrowth. Furthermore, primary motor neurons expressing mutant PFN1 display smaller growth cones with a reduced F/G-actin ratio. These observations further document that cytoskeletal pathway alterations contribute to ALS pathogenesis.
Advancing interactive collaborative mediums through tele-immersive dance (TED): a symbiotic creativity and design environment for art and computer science

The Tele-immersive Dance Environment (TED) is a geographically distributed, real-time 3-D virtual room where multiple participants interact independent of physical distance. TED, a highly interactive collaborative environment, offers digital options with multiple viewpoints, enhancing the creative movement composition involved with dance choreography. A symbiotic relationship for creativity and design exists between dance artists and computer scientists as the tele-immersive environment is analyzed as a creativity and learning tool. We introduce the advancements of the interactive digital options, new interface developments, user study results, and the possibility of a computational model for human creativity through Laban Movement Analysis.
Arabic Script Recognition : A Survey

Optical character recognition (OCR) is essential in various real-world applications, such as digitizing learning resources to assist visually impaired people and transforming printed resources into electronic media. However, the development of OCR for printed Arabic script is a challenging task. These challenges are due to the specific characteristics of Arabic script. Therefore, different methods have been proposed for developing Arabic OCR systems, and this paper aims to provide a comprehensive review of these methods. This paper also discusses relevant issues of printed Arabic OCR including the challenges of printed Arabic script and performance evaluation. It concludes with a discussion of the current status of printed Arabic OCR, analyzing the remaining problems in the field of printed Arabic OCR and providing several directions for future research. Optical character recognition; arabic printed OCR; arabic text recognition; arabic OCR survey; feature extraction; segmentation; classification
Design of Compact Wide Stopband Microstrip Low-pass Filter using T-shaped Resonator

In this letter, a compact microstrip low-pass filter (LPF) using T-shaped resonator with wide stopband is presented. The proposed LPF has capability to remove the eighth harmonic and a low insertion loss of 0.12 dB. The bandstop structure using stepped impendence resonator and two open-circuit stubs are used to design a wide stopband with attenuation level better than −20 dB from 3.08 up to 22 GHz. The proposed filter with −3-dB cutoff frequency of 2.68 GHz has been designed, fabricated, and measured. The operating of the LPF is investigated based on equivalent circuit model. Simulation results are verified by measurement results and excellent agreement between them is observed.
Flocks , Herds , and Schools : A Distributed Behavioral Model

The aggregate motion of a flock of birds, a herd of land animals, or a school of fish is a beautiful and familiar part of the n a t u r a l world. But this type of complex motion is rarely seen in computer animation. This paper explores an approach based on simulation as an alternative to scripting the paths of each bird individually. The simulated flock is an elaboration of a particle system, with the simulated birds being the particles. The aggregate motion of the simulated flock is created by a distributed behavioral model much like that at work in a natural flock; the birds choose their own course. Each simulated bird is implemented as an independent actor that navigates according to its local perception of the dynamic environment, the laws of simulated physics that rule its motion, and a set of behaviors programmed into it by the "animator." The aggregate motion of the simulated flock is the result of the dense interaction of the relatively simple behaviors of the individual simulated birds.
AzureML: Anatomy of a machine learning service

In this paper, we describe AzureML, a web service that provides a model authoring environment where data scientists can create machine learning models and publish them easily (http://www.azure.com/ml). In addition, AzureML provides several distinguishing features. These include: (a) collaboration, (b) versioning, (c) visual workflows, (d) external language support, (e) push-button operationalization, (f) monetization and (g) service tiers. We outline the system overview, design principles and lessons learned in building such a system.