nils-herrmann/clean_scirepeval huggingface dataset:
Design of EMG Acquisition Circuit to Control an Antagonistic Mechanism Actuated by Pneumatic Artificial Muscles PAMs

-A pneumatically actuated antagonistic pair of muscles with joint mechanism (APMM) is supported and developed to be essential for bionic and biomimetic applications to emulate the biological muscles by realizing various kinds of locomotion based on normal electrical activity of biological muscles. This Paper aims to compare the response of antagonistic pairs of muscles mechanism (APMM) based on the pneumatic artificial muscles (PAMs) to an EMG signal that was acquired throw a designed circuit and an EMG Laboratory acquisition kit. The response is represented as a joint rotary displacement generated by the contraction and extension of the pneumatic artificial muscles. A statistical study was done to prove the efficiency of the designed circuit the response of antagonistic pairs of muscles mechanism. The statistical result showed that there is no significant difference of voltage data in both EMG acquired signal between reference kit and designed circuit. An excellent correlation behavior between the EMG control signal and the response of APMM as an angular displacement has been discussed and statistically analyzed. Index Term-Pneumatic Artificial Muscles, Biomechatronics, Electromyogram EMG, Pneumatic Proportional Directional Control Valve, Signal Processing, Bionic.
Parallel Processing of Dynamic Continuous Queries over Streaming Data Flows

More and more real-time applications need to handle dynamic continuous queries over streaming data of high density. Conventional data and query indexing approaches generally do not apply for excessive costs in either maintenance or space. Aiming at these problems, this study first proposes a new indexing structure by fusing an adaptive cell and KDB-tree, namely CKDB-tree. A cell-tree indexing approach has been developed on the basis of the CKDB-tree that supports dynamic continuous queries. The approach significantly reduces the space costs and scales well with the increasing data size. Towards providing a scalable solution to filtering massive steaming data, this study has explored the feasibility to utilize the contemporary general-purpose computing on the graphics processing unit (GPGPU). The CKDB-tree-based approach has been extended to operate on both the CPU (host) and the GPU (device). The GPGPU-aided approach performs query indexing on the host while perform streaming data filtering on the device in a massively parallel manner. The two heterogeneous tasks execute in parallel and the latency of streaming data transfer between the host and the device is hidden. The experimental results indicate that (1) CKDB-tree can reduce the space cost comparing to the cell-based indexing structure by 60 percent on average, (2) the approach upon the CKDB-tree outperforms the traditional counterparts upon the KDB-tree by 66, 75 and 79 percent in average for uniform, skewed and hyper-skewed data in terms of update costs, and (3) the GPGPU-aided approach greatly improves the approach upon the CKDB-tree with the support of only a single Kepler GPU, and it provides real-time filtering of streaming data with 2.5M data tuples per second. The massively parallel computing technology exhibits great potentials in streaming data monitoring.
Mining Personal Data Using Smartphones and Wearable Devices: A Survey

The staggering growth in smartphone and wearable device use has led to a massive scale generation of personal (user-specific) data. To explore, analyze, and extract useful information and knowledge from the deluge of personal data, one has to leverage these devices as the data-mining platforms in ubiquitous, pervasive, and big data environments. This study presents the personal ecosystem where all computational resources, communication facilities, storage and knowledge management systems are available in user proximity. An extensive review on recent literature has been conducted and a detailed taxonomy is presented. The performance evaluation metrics and their empirical evidences are sorted out in this paper. Finally, we have highlighted some future research directions and potentially emerging application areas for personal data mining using smartphones and wearable devices.
Designing for social play in co-located mobile games

In this paper we explore how mobile devices and co-location in mobile contexts contribute social play in game design, addressing the limited understanding of social interactivity in mobile games. Using the Mechanics-Dynamics-Aesthetics (MDA) framework, we code four games illustrating effective use of mobile, social, and co-located elements. Subsequently, we analyse and discuss this data to identify generalisability in these games. In our discussion we identify how these findings address game design problems of designing collaborative games. Furthermore, we contribute to theory of designing for social play from the perspectives of co-located mobile contexts in game design by identifying how mobility: (1) affords co-locating with other players in public spaces, (2) supports physical interactions using spatial context and players bodies, extending games beyond their formal system, (3) supports asymmetric design with information and interaction. Finally we contribute the concept of a pocket magic circle, a particular magic circle, to support a focus on game design leveraging these intersecting properties.
Whispered speech recognition using deep denoising autoencoder

Recently Deep Denoising Autoencoders (DDAE) have shown state-of-the-art performance on various machine learning tasks. In this paper, the authors extended this approach to whispered speech recognition which is one of the most challenging problems in Automatic Speech Recognition (ASR). Namely, due to the profound differences between acoustic characteristics of neutral and whispered speech, the performance of traditional ASR systems trained on neutral speech degrades significantly when whisper is applied. This mismatch between training and testing is successfully alleviated with the new proposed system based on deep learning, where DDAE is applied for generating whisper-robust cepstral features. This system was tested and compared in terms of word recognition accuracy with conventional Hidden Markov Model (HMM) speech recognizer in an isolated word recognition task with a real database of whispered speech (WhiSpe). Three types of cepstral coefficients were used in the experiments: MFCC (Mel-Frequency Cepstral Coefficients), TECC (Teager-Energy Cepstral Coefficients) and TEMFCC (Teager-based Mel-Frequency Cepstral Coefficients). The experimental results showed that the proposed system significantly improves whisper recognition accuracy and outperforms traditional HMM-MFCC baseline, resulting in an absolute 31% improvement of whisper recognition accuracy. The highest word recognition rate of 92.81% in whispered speech was achieved with TECC feature.
When Sales and Marketing Align : Impact on Performance

Without sales and marketing working to produce revenue, the firm ceases to exist. Yet, given the magnitude of what’s at stake, these two functions are often at odds with one another to the detriment of performance. This article reviews previous studies that investigate conflict, collaboration, and integration between the sales and marketing functions. Next, hypotheses are developed relating alignment between the sales and marketing functions and key organizational performance objectives. Results of an empirical study encompassing 821 respondents demonstrate strong support for improved performance on eight key outcomes for firms where sales and marketing were aligned. The findings connote a potential high return on investment for organizations devoting time and resources to improving the relationship between the sales and marketing functions.
A Dual-Band CMOS Voltage-Controlled Oscillator Implemented With Dual-Resonance LC Tank

A new fully integrated, dual-band CMOS voltage controlled oscillator (VCO) is presented. The VCO is composed of n-core cross-coupled Colpitts VCOs and was implemented in 0.18 mum CMOS technology with 0.8 V supply voltage. The circuit allows the VCO to operate at two resonant frequencies with a common LC tank. The VCO has two control inputs, one for continuous control of the output frequency and one for band switching. This VCO is configured with 5 GHz and 12 GHz frequency bands with differential outputs. The dual-band VCO operates in 4.78-5.19 GHz and 12.19-12.61 GHz. The phase noises of the VCO operating at 5.11 and 12.2 GHz are -117.16 dBc/Hz and -112.15 dBc/Hz at 1 MHz offset, respectively, while the VCO draws 3.2/2.72 mA and 2.56/2.18 mW consumption at low/high frequency band from a 0.8 V supply.
Toward Future Scenario Generation: Extracting Event Causality Exploiting Semantic Relation, Context, and Association Features

We propose a supervised method of extracting event causalities like conduct slash-and-burn agriculture →exacerbate desertification from the web using semantic relation (between nouns), context, and association features. Experiments show that our method outperforms baselines that are based on state-of-the-art methods. We also propose methods of generatingfuture scenarioslike conduct slash-and-burn agriculture →exacerbate desertification→increase Asian dust (from China)→asthma gets worse . Experiments show that we can generate 50,000 scenarios with 68% precision. We also generated a scenario deforestation continues→global warming worsens →sea temperatures rise →vibrio parahaemolyticus fouls (water) , which is written in no document in our input web corpus crawled in 2007. But the vibrio risk due to global warming was observed in Baker-Austin et al. (2013). Thus, we “predicted” the future event sequence in a sense.
The Highly Adaptive SDM Hand: Design and Performance Evaluation

The inherent uncertainty associated with unstructured environments makes establishing a successful grasp difficult. Traditional approaches to this problem involve hands that are complex, fragile, require elaborate sensor suites, and are difficult to control. Alternatively, by carefully designing the mechanical structure of the hand to incorporate features such as compliance and adaptability, the uncertainty inherent in unstructured grasping tasks can be more easily accommodated. In this paper, we demonstrate a novel adaptive and compliant grasper that can grasp objects spanning a wide range of size, shape, mass, and position/orientation using only a single actuator. The hand is constructed using polymer-based Shape Deposition Manufacturing (SDM) and has superior robustness properties, making it able to withstand large impacts without damage. We also present the results of two experiments to demonstrate that the SDM Hand can reliably grasp objects in the presence of large positioning errors, while keeping acquisition contact forces low. In the first, we evaluate the amount of allowable manipulator positioning error that results in a successful grasp. In the second experiment, the hand autonomously grasps a wide range of spherical objects positioned randomly across the workspace, guided by only a single image from an overhead camera, using feed-forward control of the hand. The   http://www.sagepub.co.uk/journalsPermissions.nav Figures 1–3, 6, 7, 9, 12, 13, 15, 16 appear in color online: http://ijr.sagepub.com KEY WORDS—adaptive, compliant, design and control, grasping, manipulation, mechanics, mechanism design, multifingered hands, robot, underactuated, underactuated robots, unstructured environments
Object tracking in an outdoor environment using fusion of features and cameras

This paper presents methods for tracking moving objects in an outdoor environment. A robust tracking is achieved using feature fusion and multiple cameras. The proposed method integrates spatial position, shape and color information to track object blobs. The trajectories obtained from individual cameras are incorporated by an extended Kalman filter (EKF) to resolve object occlusion. Our results show that integrating simple features makes the tracking effective and that EKF improves the tracking accuracy when long-term or temporary occlusion occurs. The tracked objects are successfully classified into three categories: single person, people group, or vehicle.
Deep Unsupervised Learning of Visual Similarities

Exemplar learning of visual similarities in an unsupervised manner is a problem of paramount importance to Computer Vision. In this context, however, the recent breakthrough in deep learning could not yet unfold its full potential. With only a single positive sample, a great imbalance between one positive and many negatives, and unreliable relationships between most samples, training of Convolutional Neural networks is impaired. In this paper we use weak estimates of local similarities and propose a single optimization problem to extract batches of samples with mutually consistent relations. Conflicting relations are distributed over different batches and similar samples are grouped into compact groups. Learning visual similarities is then framed as a sequence of categorization tasks. The CNN then consolidates transitivity relations within and between groups and learns a single representation for all samples without the need for labels. The proposed unsupervised approach has shown competitive performance on detailed posture analysis and object classification.
Sound and Music Recommendation with Knowledge Graphs

The Web has moved, slowly but steadily, from a collection of documents towards a collection of structured data. Knowledge graphs have then emerged as a way of representing the knowledge encoded in such data as well as a tool to reason on them in order to extract new and implicit information. Knowledge graphs are currently used, for example, to explain search results, to explore knowledge spaces, to semantically enrich textual documents, or to feed knowledge-intensive applications such as recommender systems. In this work, we describe how to create and exploit a knowledge graph to supply a hybrid recommendation engine with information that builds on top of a collections of documents describing musical and sound items. Tags and textual descriptions are exploited to extract and link entities to external graphs such as WordNet and DBpedia, which are in turn used to semantically enrich the initial data. By means of the knowledge graph we build, recommendations are computed using a feature combination hybrid approach. Two explicit graph feature mappings are formulated to obtain meaningful item feature representations able to catch the knowledge embedded in the graph. Those content features are further combined with additional collaborative information deriving from implicit user feedback. An extensive evaluation on historical data is performed over two different datasets: a dataset of sounds composed of tags, textual descriptions, and user’s download information gathered from Freesound.org and a dataset of songs that mixes song textual descriptions with tags and user’s listening habits extracted from Songfacts.com and Last.fm, respectively. Results show significant improvements with respect to state-of-the-art collaborative algorithms in both datasets. In addition, we show how the semantic expansion of the initial descriptions helps in achieving much better recommendation quality in terms of aggregated diversity and novelty.
A corpus-linguistic analysis of English-ic vs-ical adjectives

1. It turns out to be very difficult to detect any pattern governing the distribution of suffixes: when does an adjective end in -ic only (cf acrobatic/*acrobatical) and when does it end in -ical only (*zoologic/zoological)? 2. There are cases where one adjective root takes both suffixes (electric(al), historic(al) etc), which raise further, even more complex questions: a. are the two forms that constitute a pair synonymous? Put differently, to what degree are the adjective forms differentiated today? – if they are not synonymous, b. does each suffix contribute some constant meaning component according to which the adjectives constituting a pair can be reliably distinguished or is there some other possibility to distinguish between the different adjective forms?
Identifying prosumer's energy sharing behaviours for forming optimal prosumer-communities

Smart Grid (SG) achieves bidirectional energy and information flow between the energy user and the utility grid, allowing energy users not only to consume energy, but also to generate the energy and share with the utility grid or with other energy consumers. This type of energy user who consumes energy and who also can generate the energy is called the “prosumer”. The sustainability of the SG energy sharing process heavily depends on its participating prosumers, making prosumer participation and management schemes crucial within the energy sharing approaches. However in literature, there is very little attention on prosumer management schemes. The contribution of this article is twofold. First, this article introduces a novel concept of participating and managing the prosumers in the SG energy sharing process in the form of autonomous, intelligent goal-oriented virtual communities. Here, the community of prosumers can collectively increase the amount of power to be auctioned or bought offering higher bargaining power, thereby settling for a higher price per kilowatt in long-term. According to the literature, this research is the first of this type introducing a community approach for prosumer management. The initial step to build an effective prosumer-community is the identification of those prosumers who would be suitable to make efficient prosumer communities. This leads the necessity of identifying parameters that influence the energy sharing behaviours of prosumers. The second contribution of this article is that, this comprehensively analyzes the different parameters influencing the prosumer's energy sharing behaviours and thus presents multi-agent architecture for optimal prosumer-community formation.
BIRDS OF A FEATHER : Homophily in Social Networks

■ Abstract Similarity breeds connection. This principle—the homophily principle—structures network ties of every type, including marriage, friendship, work, advice, support, information transfer, exchange, comembership, and other types of relationship. The result is that people’s personal networks are homogeneous with regard to many sociodemographic, behavioral, and intrapersonal characteristics. Homophily limits people’s social worlds in a way that has powerful implications for the information they receive, the attitudes they form, and the interactions they experience. Homophily in race and ethnicity creates the strongest divides in our personal environments, with age, religion, education, occupation, and gender following in roughly that order. Geographic propinquity, families, organizations, and isomorphic positions in social systems all create contexts in which homophilous relations form. Ties between nonsimilar individuals also dissolve at a higher rate, which sets the stage for the formation of niches (localized positions) within social space. We argue for more research on: (a) the basic ecological processes that link organizations, associations, cultural communities, social movements, and many other social forms; (b) the impact of multiplex ties on the patterns of homophily; and (c) the dynamics of network change over time through which networks and other social entities co-evolve.
IoT system for Human Activity Recognition using BioHarness 3 and Smartphone

This paper presents an Internet of Things (IoT) approach to Human Activity Recognition (HAR) using remote monitoring of vital signs in the context of a healthcare system for self-managed chronic heart patients. Our goal is to create a HAR-IoT system using learning algorithms to infer the activity done within 4 categories (lie, sit, walk and run) as well as the time consumed performing these activities and, finally giving feedback during and after the activity. Alike in this work, we provide a comprehensive insight on the cloud-based system implemented and the conclusions after implementing two different learning algorithms and the results of the overall system for larger implementations.
A planar wideband Quasi-Yagi antenna with high gain and FTBR

This paper presents the design of a planar Quasi-Yagi antenna capable of achieving high Front-to-Back ratio (i.e. FTBR > 34 dB), high gain (i.e. > 6.4 dBi) and relatively wide bandwidth (i.e. up to almost 1 GHz). This antenna is designed to cover the 2.45 GHz ISM band. The overall dimensions of the antenna are 69 × 64 × 1.575 mm3. The antenna is made up of 5 director elements, 1 driven dipole and a reflector ground plane. Wide bandwidth is made possible through the strong coupling of the first director element and the driven dipole element. The antenna is optimized in order to achieve the above mentioned performance criteria. This antenna can be very useful for applications requiring highly directive radiation patterns such as point-to-point communications and wireless power transfer. The radiation characteristics of this antenna can be further enhanced by building a phased array based on it.
Combining template tracking and laser peak detection for 3D reconstruction and grasping in underwater environments

Autonomous grasping of unknown objects by a robot is a highly challenging skill that is receiving increasing attention in the last years. This problem becomes still more challenging (and less explored) in underwater environments, with highly unstructured scenarios, limited availability of sensors and, in general, adverse conditions that affect in different degree the robot perception and control systems. This paper describes an approach for semi-autonomous grasping and recovery on underwater unknown objects from floating vehicles. A laser stripe emitter is attached to a robot forearm that performs a scan of a target of interest. This scan is captured by a camera that also estimates the motion of the floating vehicle while doing the scan. The scanned points are triangulated and transformed according to the motion estimation, thus recovering partial 3D structure of the scene with respect to a fixed frame. A user then indicates the part where to grab the object, and the final grasp is automatically planned on that area. The approach herein presented is tested and validated in water tank conditions.
Extracting a biologically relevant latent space from cancer transcriptomes with variational autoencoders

The Cancer Genome Atlas (TCGA) has profiled over 10,000 tumors across 33 different cancer-types for many genomic features, including gene expression levels. Gene expression measurements capture substantial information about the state of each tumor. Certain classes of deep neural network models are capable of learning a meaningful latent space. Such a latent space could be used to explore and generate hypothetical gene expression profiles under various types of molecular and genetic perturbation. For example, one might wish to use such a model to predict a tumor's response to specific therapies or to characterize complex gene expression activations existing in differential proportions in different tumors. Variational autoencoders (VAEs) are a deep neural network approach capable of generating meaningful latent spaces for image and text data. In this work, we sought to determine the extent to which a VAE can be trained to model cancer gene expression, and whether or not such a VAE would capture biologically-relevant features. In the following report, we introduce a VAE trained on TCGA pan-cancer RNA-seq data, identify specific patterns in the VAE encoded features, and discuss potential merits of the approach. We name our method "Tybalt" after an instigative, cat-like character who sets a cascading chain of events in motion in Shakespeare's "Romeo and Juliet". From a systems biology perspective, Tybalt could one day aid in cancer stratification or predict specific activated expression patterns that would result from genetic changes or treatment effects.