nils-herrmann/clean_scirepeval huggingface dataset:
SAR Image Despeckling Using a Convolutional Neural Network

Synthetic aperture radar (SAR) images are often contaminated by a multiplicative noise known as speckle. Speckle makes the processing and interpretation of SAR images difficult. We propose a deep-learning-based approach called, image despeckling convolutional neural network (ID-CNN), for automatically removing speckle from the input noisy images. In particular, ID-CNN uses a set of convolutional layers along with batch normalization and rectified linear unit activation function and a componentwise division residual layer to estimate speckle and it is trained in an end-to-end fashion using a combination of Euclidean loss and total variation loss. Extensive experiments on synthetic and real SAR images show that the proposed method achieves significant improvements over the state-of-the-art speckle reduction methods.
Simulation of wide area measurement system with optimal phasor measurement unit location

Traditional supervisory control and data acquisition (SCADA) measurements have been providing information regarding different power system parameters for decades. These measurements are typically taken once every 4 to 10 seconds offering a steady state view of the power system behaviour. However, for monitoring and control of large grid only steady state information may not be sufficient. Phasor measurement units (PMUs) provide dynamic view of the grid. Since PMUs are costly device, optimal placement of PMUs is a thrust areas of research in power system planning. This has been already investigated in previous research findings by different optimization technique. This paper extensively corroborates a simulation model of a wide area network with optimal PMU location. The purpose is to experimentally determine the ability of PMU to observe the entire network from its optimal location. The proposed methodology is implemented and investigated on IEEE six bus system using MATLAB/Simulink.
Modular joint design for performance enhanced humanoid robot LOLA

The paper presents the performance enhanced humanoid robot LOLA which is currently being manufactured. The goal of the project is the realization of a fast, human-like walking motion. The robot is characterized by its lightweight construction, a modular, multi-sensory joint design with brushless motors and an electronics architecture using decentralized joint controllers. The fusion of motor, gear and sensors into a highly integrated, mechatronic joint module has several advantages for the whole system, including high power density, good dynamic performance and reliability. Additional degrees of freedom are introduced in elbow, waist and toes. Linear actuators are employed for the knee joints for a better mass distribution in the legs
A Dependency Graph Approach for Fault Detection and Localization Towards Secure Smart Grid

Fault diagnosis in power grids is known to be challenging, due to the massive scale and spatial coupling therein. In this study, we explore multiscale network inference for fault detection and localization. Specifically, we model the phasor angles across the buses as a Markov random field (MRF), where the conditional correlation coefficients of the MRF are quantified in terms of the physical parameters of power systems. Based on the MRF model, we then study decentralized network inference for fault diagnosis, through change detection and localization in the conditional correlation matrix of the MRF. Particularly, based on the hierarchical topology of practical power systems, we devise a multiscale network inference algorithm that carries out fault detection and localization in a decentralized manner. Simulation results are used to demonstrate the effectiveness of the proposed approach.
An improved fuzzy clustering method using modified Fukuyama-Sugeno cluster validity index

The objective of clustering algorithms is to group similar patterns in one class and dissimilar patterns in disjoint classes. This article proposes a novel algorithm for fuzzy partitional clustering with an aim to minimize a composite objective function, defined using the Fukuyama-Sugeno cluster validity index. The optimization of this objective function tries to minimize the separation between clusters of a data set and maximize the compactness of a certain cluster. But in certain cases, such as a data set having overlapping clusters, this approach leads to poor clustering results. Thus we introduce a new parameter in the objective function which enables us to yield more accurate clustering results. The algorithm has been validated with some artificial and real world datasets.
Sensor Log: A mobile data collection and annotation application

Smartphones equipped with sensors can be used for monitoring simple daily activities (e.g., walking, climbing stairs etc.). The data collected via sensors need to be partially or completely annotated in order to be used in the training phase of learning algorithms. In order to ease the time consuming and burdensome annotation process, a mobile data collection and annotation application named Sensor Log is developed. The annotation feature and its ease of use make the developed application superior to existing systems. The application can be downloaded from the Android applications distribution platform.
Speaker-listener neural coupling underlies successful communication.

Verbal communication is a joint activity; however, speech production and comprehension have primarily been analyzed as independent processes within the boundaries of individual brains. Here, we applied fMRI to record brain activity from both speakers and listeners during natural verbal communication. We used the speaker's spatiotemporal brain activity to model listeners' brain activity and found that the speaker's activity is spatially and temporally coupled with the listener's activity. This coupling vanishes when participants fail to communicate. Moreover, though on average the listener's brain activity mirrors the speaker's activity with a delay, we also find areas that exhibit predictive anticipatory responses. We connected the extent of neural coupling to a quantitative measure of story comprehension and find that the greater the anticipatory speaker-listener coupling, the greater the understanding. We argue that the observed alignment of production- and comprehension-based processes serves as a mechanism by which brains convey information.
An unusual vesical calculus.

We report a 52 year old patient presenting with a bladder stone formed over a migrated intrauterine device (Copper-T). Her history was pertinent for intrauterine contraceptive (IUCD) device placement 10 years back. Investigations included plain ultrasound of abdomen, X-ray of abdomen, urinalysis, and urine culture. Ultrasound and plain X-ray of the pelvis confirmed a bladder stone formed over a migrated copper-T intrauterine device. The device was removed through suprapubic cystolithotomy. Of all the reported cases of vesical stone formation over a migrated IUCD, this case is unique as the patient was an elderly - 52 year old - female. All previously reported cases are of younger age.
Collaborative Creation of Communal Hierarchical Taxonomies in Social Tagging Systems

Collaborative tagging systems—systems where many casual users annotate objects with free-form strings (tags) of their choosing—have recently emerged as a powerful way to label and organize large collections of data. During our recent investigation into these types of systems, we discovered a simple but remarkably effective algorithm for converting a large corpus of tags annotating objects in a tagging system into a navigable hierarchical taxonomy of tags. We first discuss the algorithm and then present a preliminary model to explain why it is so effective in these types of systems.
Data quality management, data usage experience and acquisition intention of big data analytics

Big data analytics associated with database searching, mining, and analysis can be seen as an innovative IT capability that can improve firm performance. Even though some leading companies are actively adopting big data analytics to strengthen market competition and to open up new business opportunities, many firms are still in the early stage of the adoption curve due to lack of understanding of and experience with big data. Hence, it is interesting and timely to understand issues relevant to big data adoption. In this study, a research model is proposed to explain the acquisition intention of big data analytics
Supernetwork Model of Intra-organizational Knowledge Transfer

Knowledge transfer has become a hot issue for knowledge management. Nevertheless the application of heterogeneous network model to the analysis of the internal processes of intra-organization knowledge transfer is still rare. In this paper, we propose a super network model of intra-organizational knowledge transfer. The model is composite of a social network and a knowledge network. It consists of individual nodes, knowledge nodes, and the directed or undirected edges among them, which represent the organization members, the organization knowledge and their complex relationship respectively. The results of model simulation on the Net logo platform show that the higher the average degree of nodes is in the knowledge transfer network, the more innovation is created by the organization. Moreover, the results demonstrate that the knowledge of network achieve a specific equilibrium more quickly if the number of initial transfer nodes is higher. The proposed super network model provides a novel thinking and method for further in-depth study of intra-organizational knowledge transfer.
Confidence Measures for Neural Network Classifiers

Neural Networks are commonly used in classification and decision tasks. In this paper, we focus on the problem of the local confidence of their results. We review some notions from statistical decision theory that offer an insight on the determination and use of confidence measures for classification with Neural Networks. We then present an overview of the existing confidence measures and finally propose a simple measure which combines the benefits of the probabi-listic interpretation of network outputs and the estimation of the quality of the model by bootstrap error estimation. We discuss empirical results on a real-world application and an artificial problem and show that the simplest measure behaves often better than more sophisticated ones, but may be dangerous under certain situations.
Movement and respiration detection using statistical properties of the FMCW radar signal

This paper presents a 24 GHz FMCW radar system for detection of movement and respiration using change in the statistical properties of the received radar signal, both amplitude and phase. We present the hardware and software segments of the radar system as well as algorithms with measurement results for two distinct use-cases: 1. FMCW radar as a respiration monitor and 2. a dual-use of the same radar system for smart lighting and intrusion detection. By using change in statistical properties of the signal for detection, several system parameters can be relaxed, including, for example, pulse repetition rate, power consumption, computational load, processor speed, and memory space. We will also demonstrate, that the capability to switch between received signal strength and phase difference enables dual-use cases with one requiring extreme sensitivity to movement and the other robustness against small sources of interference.
Waste Management as an IoT-Enabled Service in Smart Cities

Intelligent Transportation Systems (ITS) enable new services within Smart Cities. Efficient Waste Collection is considered a fundamental service for Smart Cities. Internet of Things (IoT) can be applied both in ITS and Smart cities forming an advanced platform for novel applications. Surveillance systems can be used as an assistive technology for high Quality of Service (QoS) in waste collection. Specifically, IoT components: (i) RFIDs, (ii) sensors, (iii) cameras, and (iv) actuators are incorporated into ITS and surveillance systems for efficient waste collection. In this paper we propose an advanced Decision Support System (DSS) for efficient waste collection in Smart Cities. The system incorporates a model for data sharing between truck drivers on real time in order to perform waste collection and dynamic route optimization. The system handles the case of ineffective waste collection in inaccessible areas within the Smart City. Surveillance cameras are incorporated for capturing the problematic areas and provide evidence to the authorities. The waste collection system aims to provide high quality of service to the citizens of a Smart City.
Parental stress, family quality of life, and family-teacher partnerships: Families of children with autism spectrum disorder.

Reducing parental stress and improving family quality of Life (FQOL) are continuing concerns for families of children with autism spectrum disorder (ASD). Family-teacher partnerships have been identified as a positive factor to help parents reduce their stress and improve their FQOL. However, the interrelations among parental stress, FQOL, and family-teacher partnerships need to be further examined so as to identify the possible paths to help parents reduce their stress and improve their FQOL. The purpose of this study was to examine the interrelations among these three variables.



A total of 236 parents of school children with ASD completed questionnaires, which included three measures: (a) the Beach Center Family Quality of Life Scale, (b) the Parental Stress Scale, and (c) the Beach Center Family-Professional Partnerships Scale. The structural equation modeling was used to analyze the interrelations among these three variables.



Perceived parental stress had a direct effect on parental satisfaction concerning FQOL and vice versa. Perceived family-teacher partnerships had a direct effect on FQOL, but did not have a direct effect on parental stress. However, family-teacher partnerships had an indirect effect on parental stress through FQOL.



Reducing parental stress could improve FQOL for families of children with ASD and vice versa. Strong family-teacher partnerships could help parents of children with ASD improve their FQOL and indirectly reduce their stress.
Improving Energy Efficiency of MQTT-SN in Lossy Environments Using Seed-Based Network Coding

This paper presents an energy-efficient solution to overcome packet loss in Wireless Sensor Networks (WSNs) by adding seed-based Random Linear Network Coding to MQTT for Sensor Networks (MQTT-SN). Whereas most sensors integrated in common WSN devices consume little energy, using the radio is costly. To increase battery lifetime, devices try to minimize their radio uptime, while still satisfy timeliness and reliability of delivered data. The proposed approach uses an optimized seed-based intrasession Network Coding scheme for Forward Error Correction to shorten the sensor node's radio uptime, reducing its power consumption. The presented approach is conform to the MQTT-SN specification and, thus, interoperable with existing systems. Since the implementation is based on the application layer, it is seamlessly deployable via Over-The-Air-Programming. The presented evaluation is based on collected traces from a real-world WSN deployment in the context of Precision Agriculture. Radio uptime and power consumption measurements in an experimental testbed confirm the achieved benefits.
Verilog HDL model based thermometer-to-binary encoder with bubble error correction

This paper compares several approaches to come up with the Verilog HDL model of the thermometer-to-binary encoder with bubble error correction. It has been demonstrated that implementations of different ideas to correct bubble errors yield circuits whose parameters tremendously vary in delay, area and power consumption. The shortest delay is achieved for the design synthesized from the model which mimics a human reading temperature on classic liquid-in-glass thermometer.
0.18um BCD technology with best-in-class LDMOS from 6 V to 45 V

We propose a novel nLDMOS structure and a design concept in BCD technology with the best-in-class performance. The drift profile is optimized and the multi-oxide in the drift region is adopted to approach the RESURF limit of on-resistance vs. BVdss characteristic (i.e., 36V DMOS has a Ron_sp of 20mohm-mm2 with a BVdss of 50V; 45V DMOS has a Ron_sp of 28mohm-mm2 with a BVdss of 65V). Moreover, this modification requires merely three extra masks in the Non-Epitaxy LV process to achieve this improvement. Therefore it is not only a high performance but also a low cost solution.
An Efficient Method for Traffic Sign Recognition Based on Extreme Learning Machine

This paper proposes a computationally efficient method for traffic sign recognition (TSR). This proposed method consists of two modules: 1) extraction of histogram of oriented gradient variant (HOGv) feature and 2) a single classifier trained by extreme learning machine (ELM) algorithm. The presented HOGv feature keeps a good balance between redundancy and local details such that it can represent distinctive shapes better. The classifier is a single-hidden-layer feedforward network. Based on ELM algorithm, the connection between input and hidden layers realizes the random feature mapping while only the weights between hidden and output layers are trained. As a result, layer-by-layer tuning is not required. Meanwhile, the norm of output weights is included in the cost function. Therefore, the ELM-based classifier can achieve an optimal and generalized solution for multiclass TSR. Furthermore, it can balance the recognition accuracy and computational cost. Three datasets, including the German TSR benchmark dataset, the Belgium traffic sign classification dataset and the revised mapping and assessing the state of traffic infrastructure (revised MASTIF) dataset, are used to evaluate this proposed method. Experimental results have shown that this proposed method obtains not only high recognition accuracy but also extremely high computational efficiency in both training and recognition processes in these three datasets.
Socio-cognitive gamification: general framework for educational games

Gamification of learning material has received much interest from researchers in the past years. This paper aims to further improve such learning experience by applying socio-cognitive gamification to educational games. Dynamic difficulty adjustment (DDA) is a well-known tool in optimizing gaming experience. It is a process to control the parameters in a video game automatically based on user experience in real-time. This method can be extended by using a biofeedback-approach, where certain aspects of the player’s ability is estimated based on physiological measurement (e.g. eye tracking, ECG, EEG). Here, we outline the design of a biofeedback-based framework that supports dynamic difficulty adjustment in educational games. It has a universal architecture, so the concept can be employed to engage users in non-game contexts as well. The framework accepts input from the games, from the physiological sensors and from the so-called supervisor unit. This special unit empowers a new social aspect by enabling another user to observe or intervene during the interaction. To explain the game-user interaction itself in educational games we propose a hybrid model.
Modeling Social Perception of Faces [Social Sciences]

The face is our primary source of visual information for identifying people and reading their emotional and mental states. With the exception of prosopagnosics (who are unable to recognize faces) and those suffering from such disorders of social cognition as autism, people are extremely adept at these two tasks. However, our cognitive powers in this regard come at the price of reading too much into the human face. The face is often treated as a window into a person's true nature. Given the agreement in social perception of faces, this paper discusses that it should be possible to model this perception.
Public beliefs about and attitudes towards people with mental illness: a review of population studies.

To provide a review of population-based attitude research in psychiatry during the past 15 years.



An electronic search using PubMed, Medline, and Academic Search Premier plus a hand search of the literature was carried out for studies on public beliefs about mental illness and attitudes towards the mentally ill published between 1990 and 2004.



Thirty-three national studies and 29 local and regional studies were identified, mostly from Europe. Although the majority are of descriptive nature, more recent publications include studies testing theory-based models of the stigmatization of mentally ill people, analyses of time trends and cross-cultural comparisons, and evaluations of antistigma interventions.



Attitude research in psychiatry made considerable progress over the past 15 years. However, there is still much to be done to provide an empirical basis for evidence-based interventions to reduce misconceptions about mental illness and improve attitudes towards persons with mental illness.
Text comparison using word vector representations and dimensionality reduction

This paper describes a technique to compare large text sources using word vector representations (word2vec) and dimensionality reduction (tSNE) and how it can be implemented using Python. The technique provides a bird’s-eye view of text sources, e.g. text summaries and their source material, and enables users to explore text sources like a geographical map. Word vector representations capture many linguistic properties such as gender, tense, plurality and even semantic concepts like "capital city of". Using dimensionality reduction, a 2D map can be computed where semantically similar words are close to each other. The technique uses the word2vec model from the gensim Python library and t-SNE from scikit-learn.
The Enactive Approach to Architectural Experience: A Neurophysiological Perspective on Embodiment, Motivation, and Affordances

Over the last few years, the efforts to reveal through neuroscientific lens the relations between the mind, body, and built environment have set a promising direction of using neuroscience for architecture. However, little has been achieved thus far in developing a systematic account that could be employed for interpreting current results and providing a consistent framework for subsequent scientific experimentation. In this context, the enactive perspective is proposed as a guide to studying architectural experience for two key reasons. Firstly, the enactive approach is specifically selected for its capacity to account for the profound connectedness of the organism and the world in an active and dynamic relationship, which is primarily shaped by the features of the body. Thus, particular emphasis is placed on the issues of embodiment and motivational factors as underlying constituents of the body-architecture interactions. Moreover, enactive understanding of the relational coupling between body schema and affordances of architectural spaces singles out the two-way bodily communication between architecture and its inhabitants, which can be also explored in immersive virtual reality settings. Secondly, enactivism has a strong foothold in phenomenological thinking that corresponds to the existing phenomenological discourse in architectural theory and qualitative design approaches. In this way, the enactive approach acknowledges the available common ground between neuroscience and architecture and thus allows a more accurate definition of investigative goals. Accordingly, the outlined model of architectural subject in enactive terms-that is, a model of a human being as embodied, enactive, and situated agent, is proposed as a basis of neuroscientific and phenomenological interpretation of architectural experience.