nils-herrmann/clean_scirepeval huggingface dataset:
User experiences and expectations of vibrotactile, thermal and squeeze feedback in interpersonal communication

Katja Suhonen Tampere University of Technology, Human-Centered Technology, P.O.Box 589, 33101 Tampere, Finland katja.suhonen@tut.fi Kaisa Väänänen-Vainio-Mattila Tampere University of Technology, Human-Centered Technology, P.O.Box 589, 33101 Tampere, Finland kaisa.vaananen-vainiomattila@tut.fi Kalle Mäkelä Tampere University of Technology, Department of Electronics, P.O.Box 692, 33101 Tampere, Finland kalle.makela@tut.fi
Symbolic representations of time series applied to biometric recognition based on ECG signals

One reason for researching new biometric modalities is to improve the capabilities of security systems against threats. Biometric modalities based on biomedical signals, in particular the electrocardiogram signal (ECG), have been widely adopted. These can be represented by time series. However, in this context, a critical issue is how to extract features from ECG signals effectively. Several techniques have been put forward regarding the best way to represent time series, in particular techniques that are based on symbolic values, and significant results have been achieved by means of these techniques for addressing different types of problems. In this paper, we present twenty symbolic representations of time series applied to the extraction of nonfiducial features from ECG signals that aim at biometric recognition. In addition, we put forward three novel symbolic representations, namely, Representation based on Kmeans (R-Kmeans), Symbolic Aggregate approXimation based on Kmeans (SAX-Kmeans), and Extended Symbolic Aggregate approXimation based on Kmeans (ESAX-Kmeans). Experimental results conducted on two publicly available datasets indicate that the novel representations can improve the performance of recognition compared with the others.
Raspberry Pi based interactive home automation system through E-mail

Home automation is becoming more and more popular day by day due to its numerous advantages. This can be achieved by local networking or by remote control. This paper aims at designing a basic home automation application on Raspberry Pi through reading the subject of E-mail and the algorithm for the same has been developed in python environment which is the default programming environment provided by Raspberry Pi. Results show the efficient implementation of proposed algorithm for home automation. LEDs were used to indicate the switching action.
Software-Defined Satellite: A New Concept for Space Information System

To resolve the current drawbacks of the construction of space information systems (SISs), the concept of software-defined satellite (SDS) is proposed in this paper with detailed discussion of the payload of SDS, the architecture of which is based on the software-defined radio technology. Top-level design issues of SDS based integrated SIS (SDS-BI-SIS) are discussed and new features provided by the SDS-BI-SIS are also given.
Towards adaptive health-aware charging of Li-ion batteries: A real-time predictive control approach using first-principles models

Today's charging strategies of Li-ion batteries are often designed off-line and do not balance optimal fast charging and battery lifetime. By a lack of adaptation during operation, the off-line charging strategies do not allow aging-related changes of the battery properties to be taken into account. As such, to achieve safe operation, batteries and cells are typically operated in a range that is far away from the achievable operational limits. This article presents a nonlinear predictive control strategy that allows real-time optimal health-aware charging based on detailed first-principles models. The approach balances fast charging while improving battery lifetime. By considering the on-line adjustment of battery model parameters, the approach is able to directly take aging- and environment-related changes into account.
Euler Number and Connectivity Indexes of a Three Dimensional Digital Picture

Fundamental properties of topological structure of a 3D digitized picture are presented including the concept of neighborhood and connectivity among volume cells (voxels) of 3D digitized binary pictures defined on a cubic grid, the concept of simplicial decomposition of a 3D digitized object, and two algorithms for calculating the Euler number (genus). First we define four types of connectivity. Second we present two algorithms to calculate the Euler number of a 3D figure. Thirdly we introduce new local features called the connectivity number (CN) and the connectivity index to study topological properties of a 3D object in a 3D digitized picture. The CN at a 1-voxel (=a voxel with the value 1) x is defined as the change in the Euler number of the object caused by changing the value of x into zero, that is, caused by deleting x. Finally, by using them, we prove a necessary and sufficient condition that a 1-voxel is deletable. A 1-voxel x is said to be deletable if deletion of x causes no decrease and no increase in the numbers of connected components, holes and cavities in a given 3D picture.
A review of wind power and wind speed forecasting methods with different time horizons

In recent years, environmental considerations have prompted the use of wind power as a renewable energy resource. However, the biggest challenge in integrating wind power into the electric grid is its intermittency. One approach to deal with wind intermittency is forecasting future values of wind power production. Thus, several wind power or wind speed forecasting methods have been reported in the literature over the past few years. This paper provides insight on the foremost forecasting techniques, associated with wind power and speed, based on numeric weather prediction (NWP), statistical approaches, artificial neural network (ANN) and hybrid techniques over different time-scales. An overview of comparative analysis of various available forecasting techniques is discussed as well. In addition, this paper further gives emphasis on the major challenges and problems associated with wind power prediction.
Watch Me Code: Programming Mentorship Communities on Twitch.tv

Live streaming-an emerging practice of broadcasting video of oneself in real time to an online audience-is often used by people to portray themselves engaged in a craft such as programming. Viewers of these 'creative streams' gather to watch the streamer at work and to interact with the streamer and other audience members. However, little is known about how creative streamers engage with their audience, how their viewership communities form and operate, and how creative streams may support learning. In this study, we used a participant-observer method to study game development streams on the live streaming site Twitch.tv. We found that live streams support the growth of learning-focused communities that mentor both the streamer and each other during and after streams. We show the influence of streamers in creating a space for learning and motivating learners. Finally, we discuss implications for online education and communities of practice.
In vivo drug discovery in the zebrafish

The zebrafish has become a widely used model organism because of its fecundity, its morphological and physiological similarity to mammals, the existence of many genomic tools and the ease with which large, phenotype-based screens can be performed. Because of these attributes, the zebrafish might also provide opportunities to accelerate the process of drug discovery. By combining the scale and throughput of in vitro screens with the physiological complexity of animal studies, the zebrafish promises to contribute to several aspects of the drug development process, including target identification, disease modelling, lead discovery and toxicology.
Autonomous underwater grasping using multi-view laser reconstruction

Autonomous manipulation in underwater scenarios is a highly complex task which is still poorly studied, but with a growing interest in the last years. Two of the main phases of this problem are the detection and characterization of the object which is going to be manipulated, and the planning of this manipulation. This paper presents, on the one hand, the results of a multi-view laser reconstruction approach used in a real system. This approach consists in attaching a laser emitter and a camera in the forearm of a robotic arm. Then, moving the arm, the scene is scanned and reconstructed. This configuration allows the user to reconstruct autonomously a scene from different points of view and with a high precision. On the other hand, a grasp planning algorithm has been described. This method makes possible to autonomously detect the object of interest from a scene with multiple objects, and to choose which is the best arm configuration in order to manipulate it.
A new Taxonomy of Continuous Global Optimization Algorithms

Surrogate-based optimization and nature-inspired metaheuristics have become the state of the art in solving real-world optimization problems. Still, it is difficult for beginners and even experts to get an overview that explains their advantages in comparison to the large number of available methods in the scope of continuous optimization. Available taxonomies lack the integration of surrogate-based approaches and thus their embedding in the larger context of this broad field. This article presents a taxonomy of the field, which further matches the idea of nature-inspired algorithms, as it is based on the human behavior in path finding. Intuitive analogies make it easy to conceive the most basic principles of the search algorithms, even for beginners and non-experts in this area of research. However, this scheme does not oversimplify the high complexity of the different algorithms, as the class identifier only defines a descriptive meta-level of the algorithm search strategies. The taxonomy was established by exploring and matching algorithm schemes, extracting similarities and differences, and creating a set of classification indicators to distinguish between five distinct classes. In practice, this taxonomy allows recommendations for the applicability of the corresponding algorithms and helps developers trying to create or improve their own algorithms.
A simplified space vector pulse width modulation algorithm for five level diode clamping inverter

In this paper, a simplified space vector pulse with modulation (SVPWM) method for the five level diode clamping voltage source inverter is presented. The space vector diagram of the five level inverter is simplified into that of a three level inverter. In turn, the three level inverter space vector diagram is simplified into that of two level inverter. Thus, the algorithm of five level SVPWM becomes very similar to that of conventional two level inverter SVPWM. This method can be extended easily to multi-level inverters above five level
Development of DC to Single-Phase AC Voltage Source Inverter With Active Power Decoupling Based on Flying Capacitor DC/DC Converter

In the present, a power decoupling method without additional component is proposed for a dc to single-phase ac converter, which consists of a flying capacitor dc/dc converter (FCC) and the voltage source inverter (VSI). In particular, a small flying capacitor in the FCC is used for both a boost operation and a double-line-frequency power ripple reduction. Thus, the dc-link capacitor value can be minimized in order to avoid the use of a large electrolytic capacitor. In addition, component design, of, e.g., the boost inductor and the flying capacitor, is clarified when the proposed control is applied. Experiments were carried out using a 1.5-kW prototype in order to verify the validity of the proposed control. The experimental results revealed that the use of the proposed control reduced the dc-link voltage ripple by 74.5%, and the total harmonic distortion (THD) of the inverter output current was less than 5%. Moreover, a maximum system efficiency of 95.4% was achieved at a load of 1.1 kW. Finally, the high power density design is evaluated by the Pareto front optimization. The power densities of three power decoupling topologies, such as a boost topology, a buck topology, and the proposed topology are compared. As a result, the proposed topology achieves the highest power density (5.3 kW/dm3) among the topologies considered herein.
Evaluating visual analytics with eye tracking

The application of eye tracking for the evaluation of humans' viewing behavior is a common approach in psychological research. So far, the use of this technique for the evaluation of visual analytics and visualization is less prominent. We investigate recent scientific publications from the main visualization and visual analytics conferences and journals that include an evaluation by eye tracking. Furthermore, we provide an overview of evaluation goals that can be achieved by eye tracking and state-of-the-art analysis techniques for eye tracking data. Ideally, visual analytics leads to a mixed-initiative cognitive system where the mechanism of distribution is the interaction of the user with visualization environments. Therefore, we also include a discussion of cognitive approaches and models to include the user in the evaluation process. Based on our review of the current use of eye tracking evaluation in our field and the cognitive theory, we propose directions of future research on evaluation methodology, leading to the grand challenge of developing an evaluation approach to the mixed-initiative cognitive system of visual analytics.
Telemanipulation with force-based display of proximity fields

In this paper we show and evaluate the design of a novel telemanipulation system that maps proximity values, acquired inside of a gripper, to forces a user can feel through a haptic input device. The command console is complemented by input-devices that give the user an intuitive control over parameters relevant to the system. Furthermore, proximity sensors enable the autonomous alignment/centering of the gripper to objects in user-selected DoFs with the potential of aiding the user and lowering the workload. We evaluate our approach in a user study that shows that the telemanipulation system benefits from the supplementary proximity information and that the workload can indeed be reduced when the system operates with partial autonomy.
AFRL-RQ-WP-TR-2014-0212 A BROADBAND HIGH-GAIN BILAYER LOG-PERIODIC DIPOLE ARRAY ( LPDA ) FOR ULTRA HIGH FREQUENCY ( UHF ) CONFORMAL LOAD BEARING ANTENNA STRUCTURES ( CLAS ) APPLICATIONS

A broadband high-gain bi-layer Log-PeriodicDipole-Array (LPDA) is introduced for Conformal Load Bearing Antenna Structures (CLAS) applications. Under the proposed scheme the two layers of the LPDA are printed on two separate thin dielectric substrates which are substantially separated from each other. A meander line geometry is adapted to achieve size reduction for the array. The fabricated and tested array easily exceeds more than an octave of gain, pattern and VSWR bandwidth. Index Terms — Broadband antenna, array, log-periodic, LPDA, endfire, ultrawideband, UWB, CLAS.
A Comparative Analysis of Enterprise Architecture Frameworks Based on EA Quality Attributes

Many Enterprise Architecture Frameworks (EAFs) are in use today to guide or serve as a model for the enterprise architecture development. Researchers offer general comparative information about EAF. However, there has been little work on the quality of Enterprise Architecture (EA). This study provides the characteristics of the five EAFs using comparative analysis based on Enterprise Architecture Quality Attributes (EAQAs). The attribute of EA quality was extracted from the EAFs and the definition of EAQA was defined from EA user viewpoint. The criteria for each quality attribute were developed to compare EAFs using four dimensional concepts. This paper compared several frameworks using the criteria to provide guidance in the selection of an EAF that meets the quality requirement of EA
A Single-Stage Three-Phase Grid-Connected Photovoltaic System With Modified MPPT Method and Reactive Power Compensation

Single-stage grid-connected photovoltaic (PV) systems have advantages such as simple topology, high efficiency, etc. However, since all the control objectives such as the maximum power point tracking (MPPT), synchronization with the utility voltage, and harmonics reduction for output current need to be considered simultaneously, the complexity of the control scheme is much increased. This paper presents the implementation of a single-stage three-phase grid-connected PV system. In addition to realize the aforementioned control objectives, the proposed control can also remarkably improve the stability of the MPPT method with a modified incremental conductance MPPT method. The reactive power compensation for local load is also realized, so as to alleviate grid burden. A DSP is employed to implement the proposed MPPT controller and reactive power compensation unit. Simulation and experimental results show the high stability and high efficiency of this single-stage three-phase grid-connected PV system.
The marketplace of ideology : “ Elective af fi nities ” in political psychology and their implications for consumer behavior

An abundance of research in political psychology demonstrates that leftists and rightists (or liberals and conservatives) diverge from one another in terms of: (a) personality characteristics; (b) cognitive processing styles; (c) motivational interests and concerns; (d) the prioritization of personal values; and (e) neurological structures and physiological functions. In this article, I summarize these findings and discuss some of their implications for persuasion, framing, and advertising; consumer choice, judgment, decision-making, and behavior; and customer satisfaction/ dissatisfaction and politically motivated boycotts. I conclude that the theory and practice of consumer psychology will be enriched by taking into account ideological asymmetries and the ways in which human behavior both reflects and gives rise to left–right divergence in political orientation—not only in terms of beliefs, opinions, and values but also in terms of underlying psychological processes.
A Survey on Semantic Web Search Engine

The tremendous growth in the volume of data and with the terrific growth of number of web pages, traditional search engines now a days are not appropriate and not suitable anymore. Search engine is the most important tool to discover any information in World Wide Web. Semantic Search Engine is born of traditional search engine to overcome the above problem. The Semantic Web is an extension of the current web in which information is given well-defined meaning. Semantic web technologies are playing a crucial role in enhancing traditional web search, as it is working to create machine readable data. but it will not replace traditional search engine. In this paper we made a brief survey on various promising features of some of the best semantic search engines developed so far and we have discussed the various approaches to semantic search. We have summarized the techniques, advantages of some important semantic web search engines that are developed so far.The most prominent part is that how the semantic search engines differ from the traditional searches and their results are shown by giving a sample query as input.
The Communicative Functions of Emoticons in Workplace E-Mails: : -)

CMC research presents emoticons as visual representations of writers’ emotions. We argue that the emoticons in authentic workplace e-mails do not primarily indicate writers’ emotions. Rather, they provide information about how an utterance is supposed to be interpreted. We show that emoticons function as contextualization cues, which serve to organize interpersonal relations in written interaction. They serve 3 communicative functions. First, when following signatures, emoticons function as markers of a positive attitude. Second, when following utterances that are intended to be interpreted as humorous, they are joke/irony markers. Third, they are hedges: when following expressive speech acts (such as thanks, greetings, etc.) they function as strengtheners and when following directives (such as requests, corrections, etc.) they function as softeners.
Detection of distributed denial of service attacks using machine learning algorithms in software defined networks

Software Defined Networking (SDN) is a new promising networking concept which has a centralized control over the network and separates the data and control planes. This new approach provides abstraction of lower-level functionality and allows the network administrators to initialize, control, change, and manage network behavior programmatically. The centralized control, being the major advantage of SDN can sometimes also be a major security threat. If the intruder succeeds in attacking the central controller, he would get access to the entire system. The controller is highly vulnerable to Distributed Denial of Service (DDoS) attacks which lead to exhaustion of the system resources which causes non-availability of the services given by the controller. It is critical to detect the attacks in the controller at earlier stage. Many algorithms and techniques have been discovered for this purpose. But less work has been done in the field of SDN networks. Using machine learning algorithms for classifying the connections into legitimate and illegitimate is one such solution. We use two machine learning algorithms namely, the Support Vector Machine (SVM) classifier and the Neural Network (NN) classifier to detect the suspicious and harmful connections.
Online Multi-object Tracking Using CNN-Based Single Object Tracker with Spatial-Temporal Attention Mechanism

In this paper, we propose a CNN-based framework for online MOT. This framework utilizes the merits of single object trackers in adapting appearance models and searching for target in the next frame. Simply applying single object tracker for MOT will encounter the problem in computational efficiency and drifted results caused by occlusion. Our framework achieves computational efficiency by sharing features and using ROI-Pooling to obtain individual features for each target. Some online learned target-specific CNN layers are used for adapting the appearance model for each target. In the framework, we introduce spatial-temporal attention mechanism (STAM) to handle the drift caused by occlusion and interaction among targets. The visibility map of the target is learned and used for inferring the spatial attention map. The spatial attention map is then applied to weight the features. Besides, the occlusion status can be estimated from the visibility map, which controls the online updating process via weighted loss on training samples with different occlusion statuses in different frames. It can be considered as temporal attention mechanism. The proposed algorithm achieves 34.3% and 46.0% in MOTA on challenging MOT15 and MOT16 benchmark dataset respectively.
Emergency department management of vaginal bleeding in the nonpregnant patient.

Abnormal uterine bleeding is the most common reason women seek gynecologic care, and many of these women present to an emergency department for evaluation. It is essential that emergency clinicians have a thorough understanding of the underlying physiology of the menstrual cycle to appropriately manage a nonpregnant woman with abnormal bleeding. Evidence to guide the management of nonpregnant patients with abnormal bleeding is limited, and recommendations are based mostly on expert opinion. This issue reviews common causes of abnormal bleeding, including anovulatory, ovulatory, and structural causes in both stable and unstable patients. The approach to abnormal bleeding in the prepubertal girl is also discussed. Emergency clinicians are encouraged to initiate treatment to temporize an acute bleeding episode until timely follow-up with a gynecologist can be obtained.