nils-herrmann/clean_scirepeval huggingface dataset:
Induction of depressed and elated mood by music influences the perception of facial emotional expressions in healthy subjects.

The judgement of healthy subject rating the emotional expressions of a set of schematic drawn faces is validated (study 1) to examine the relationship between mood (depressed/elated) and judgement of emotional expressions of these faces (study 2).  30 healthy subjects judged 12 faces with respect to the emotions they express (fear, happiness, anger, sadness, disgust, surprise, rejection and invitation). It was found that a particular face could reflect various emotions. All eight emotions were reflected in the set of faces and the emotions were consensually judged. Moreover, gender differences in judgement could be established.  In a cross-over design, 24 healthy subjects judged the faces after listening to depressing or elating music. The faces were subdivided in six 'ambiguous' faces (i.e., expressing similar amounts of positive and negative emotions) and six 'clear' faces (i.e., faces showing a preponderance of positive or negative emotions). In addition, these two types of faces were distinguished with respect to the intensity of emotions they express. 11 subjects who showed substantial differences in experienced depression after listening to the music were selected for further analysis. It was found that, when feeling more depressed, the subjects perceived more rejection/sadness in ambiguous faces (displaying less intensive emotions) and less invitation/happiness in clear faces. In addition, subjects saw more fear in clear faces that express less intensive emotions. Hence, results show a depression-related negative bias in the perception of facial displays.
Semantics matters : cognitively plausible delineation of city centres from point of interest data

We sketch a workflow for cognitively plausible recognition of vague geographical concepts, such as a city centre. Our approach imitates a pedestrian strolling through the streets, and comparing his/her internal cognitive model of a city centre with the stimulus from the external world to decide whether he/she is in the city centre or outside. The cognitive model of a British city centre is elicited through an online questionnaire survey and used to delineate referents of city centre from point of interest data. We first compute a measure of ‘city centre-ness’ at each location within a city, and then merge the area of high city centre-ness to a contiguous region. The process is illustrated on the example of the City of Bristol, and the computed city centre area for Bristol is evaluated by comparison to reference areas derived from alternative sources. The evaluation suggests that our approach performs well and produces a representation of a city centre that is near to people’s conceptualisation. The benefits of our work are better (and user-driven) descriptions of complex geographical concepts. We see such models as a prerequisite for generalisation over large changes in detail, and for very specific purposes.
Comparing fiducial marker systems in the presence of occlusion

A fiducial marker system is a system of unique 2D (planar) marker, which is placed in an environment and automatically will be detected with a camera with a help of a corresponding detection algorithm. Application areas of these markers include industrial systems, augmented reality, robots navigation, human-robot interaction and others. Marker system designed for such different applications must be robust to such factors as view angles, occlusions, changing distances, etc. This paper compares three existing systems of markers: ARTag, AprilTag, and CALTag. As a benchmark, we use their reliability and detection rate in presence of occlusions of various types and intensity. The paper presents experimental comparison of these markers. The marker detection was performed with a simple inexpensive Web camera.
German version of the Multidimensional Body-Self Relations Questionnaire - Appearance Scales (MBSRQ-AS): confirmatory factor analysis and validation.

The Multidimensional Body-Self Relations Questionnaire (MBSRQ) is a widely used questionnaire that measures body image as a multidimensional construct. The Appearance Scales (AS) of the MBSRQ (Appearance Evaluation, Appearance Orientation, Body Areas Satisfaction, Overweight Preoccupation and Self-Classified Weight) are subscales which facilitate a parsimonious assessment of appearance-related aspects of body image. The current study tested the psychometric properties and factor structure of a German translation of the MBSRQ-AS. Participants were n=230 female patients with the SCID diagnosis of an eating disorder and n=293 female healthy controls. In a confirmatory factor analysis, convincing goodness-of-fit indices emerged. The subscales of the questionnaire yielded good reliability and convergent and discriminant validity coefficients, with most items showing excellent characteristics.
An analysis and critique of Research through Design: towards a formalization of a research approach

The field of HCI is experiencing a growing interest in Research through Design (RtD), a research approach that employs methods and processes from design practice as a legitimate method of inquiry. We are interested in expanding and formalizing this research approach, and understanding how knowledge, or theory, is generated from this type of design research. We conducted interviews with 12 leading HCI design researchers, asking them about design research, design theory, and RtD specifically. They were easily able to identify different types of design research and design theory from contemporary and historical design research efforts, and believed that RtD might be one of the most important contributions of design researchers to the larger research community. We further examined three historical RtD projects that were repeatedly mentioned in the interviews, and performed a critique of current RtD practices within the HCI research and interaction design communities. While our critique summarizes the problems, it also shows possible directions for further developments and refinements of the approach.
Multi-vehicle detection with identity awareness using cascade Adaboost and Adaptive Kalman filter for driver assistant system

Vision-based vehicle detection is an important issue for advanced driver assistance systems. In this paper, we presented an improved multi-vehicle detection and tracking method using cascade Adaboost and Adaptive Kalman filter(AKF) with target identity awareness. A cascade Adaboost classifier using Haar-like features was built for vehicle detection, followed by a more comprehensive verification process which could refine the vehicle hypothesis in terms of both location and dimension. In vehicle tracking, each vehicle was tracked with independent identity by an Adaptive Kalman filter in collaboration with a data association approach. The AKF adaptively adjusted the measurement and process noise covariance through on-line stochastic modelling to compensate the dynamics changes. The data association correctly assigned different detections with tracks using global nearest neighbour(GNN) algorithm while considering the local validation. During tracking, a temporal context based track management was proposed to decide whether to initiate, maintain or terminate the tracks of different objects, thus suppressing the sparse false alarms and compensating the temporary detection failures. Finally, the proposed method was tested on various challenging real roads, and the experimental results showed that the vehicle detection performance was greatly improved with higher accuracy and robustness.
Fuzzy Adaptive Sliding Mode Control of 6 DOF Parallel Manipulator with Electromechanical Actuators in Cartesian Space Coordinates

This paper proposes a fuzzy adaptive sliding mode controller in Cartesian space coordinates for trajectory tracking of the 6 DOF parallel manipulator with considering dynamics of electromechanical actuators. The 6-DOF sensors, may be very expensive or impossible to find at the desired accuracies; and also, especially at constant speeds, will contain errors; therefore, it is better to use LVDT position sensors and then, solving the forward kinematics problem using an iterative artificial neural network strategy, Instead of using the Numerical methods such as the Newton-Raphson method, that heavy computational load imposes to the system. This controller consists of a sliding mode control and adaptive learning algorithms to adjust uncertain parameters of system, that relax the requirement of bounding parameter values and not depending upon any parameter initialization conditions, and fuzzy approximators to estimate the plant’s unknown nonlinear functions, and robustifying control terms to compensate of approximation errors; so that closedloop stability and finite-time convergence of tracking errors can be guaranteed. Simulation results demonstrate that the proposed control strategy can achieve favorable control performance with regard to uncertainties, nonlinearities and external disturbances.
PREDICTING HELPFUL POSTS IN OPEN-ENDED DISCUSSION FORUMS: A NEURAL ARCHITECTURE

Users participate in online discussion forums to learn from others and share their knowledge with the community. They often start a thread with a question or sharing their new findings on a certain topic. We find that, unlike Community Question Answering, where questions are mostly factoid based, the threads in a forum are often open-ended (e.g., asking for recommendations from others) without a single correct answer. In this paper, we address the task of identifying helpful posts in a forum thread, to aid users comprehend long running discussion threads, which often contain repetitive or even irrelevant posts. We propose a recurrent neural network based architecture to model (i) the relevance of a post regarding the original post starting the thread and (ii) the novelty it brings to the discussion, compared to the previous posts in the thread. Experimental results on five different types of online forum datasets show that our model significantly outperforms the state-of-the-art neural network models for text classification.
Challenges and enabling technologies for energy aware mobile radio networks

Mobile communications are increasingly contributing to global energy consumption. In this article, a holistic approach for energy efficient mobile radio networks is presented. The matter of having appropriate metrics and evaluation methods that allow assessing the energy efficiency of the entire system is discussed. The mutual supplementary saving concepts comprise component, link and network levels. At the component level the power amplifier complemented by a transceiver and a digital platform supporting advanced power management are key to efficient radio implementations. Discontinuous transmission by base stations, where hardware components are switched off, facilitate energy efficient operation at the link level. At the network level, the potential for reducing energy consumption is in the layout of networks and their management, that take into account slowly changing daily load patterns, as well as highly dynamic traffic fluctuations. Moreover, research has to analyze new disruptive architectural approaches, including multi-hop transmission, ad-hoc meshed networks, terminal-to-terminal communications, and cooperative multipoint architectures.
Ring Oscillator PUF Design and Results

A Physical Unclonable Function (PUF) is a function in some physical device that is easy to evaluate but hard to predict. In many ways it is the hardware equivalent of a mathematical one-way function. Every challenge should map to a specific response, but this mapping should be impossible to predict and different for every physical device. An on-chip PUF is very useful for several reasons. First, it provides a way to uniquely identify a given device. This could be used for secure chip authentication or for protection of Intellectual Property. Second, since an FPGA PUF is based on intrinsic randomness, it could also serve as a Random Number Generator. Finally, and most importantly, a PUF provides all this functionality without storing any secret information on the chip. Most solutions to the previously listed problems involve storing some type of key or seed value on the chip. This poses many security hazards. However, a PUF relies on the characteristics of the chip itself, which are considered impossible to observe and duplicate. There are a wide variety of PUF designs, but the only ones relavant to our interests are those that can be implemented on an FPGA. There are basically five designs that meet these requirements. 1)The first PUF designed for an FPGA was introduced in . The proposed ”Arbiter PUF” design can be seen in Figure 1. Two paths are created by connecting a number of ”switch delay elements” in series. Each element uses a two-to-one multiplexer to control which path is taken by the inputs. The challenge bits X are used as the select bits to the multiplexers, and the response of the PUF is based on which path leads to the faster signal propogation.
Designing an annotation scheme for summarizing Japanese judgment documents

We propose an annotation scheme for the summarization of Japanese judgment documents. This paper reports the details of the development of our annotation scheme for this task. We also conduct a human study where we compare the annotation of independent annotators. The end goal of our work is summarization, and our categories and the link system is a consequence of this. We propose three types of generic summaries which are focused on specific legal issues relevant to a given legal case.
Cluster validation techniques for genome expression data

Several clustering algorithms have been suggested to analyse genome expression data, but fewer solutions have been implemented to guide the design of clusteringbased experiments and assess the quality of their outcomes. A cluster validity framework provides insights into the problem of predicting the correct the number of clusters. This paper presents several validation techniques for gene expression data analysis. Normalisation and validity aggregation strategies are proposed to improve the prediction about the number of relevant clusters. The results obtained indicate that this systematic evaluation approach may significantly support genome expression analyses for knowledge discovery applications.
The impact of emotional intelligence on work engagement of registered nurses: the mediating role of organisational justice.

To explore the impact of emotional intelligence and organisational justice on work engagement in Chinese nurses and to examine the mediating role of organisational justice to provide implications for promoting clinical nurses' work engagement.



The importance of work engagement on nurses' well-being and quality of care has been well documented. Work engagement is significantly predicted by job resources. However, little research has concentrated simultaneously on the influence of both personal and organisational resources on nurses' work engagement.



A descriptive, cross-sectional design was employed.



A total of 511 nurses from four public hospitals were enrolled by multistage sampling. Data collection was undertaken using the Wong and Law Emotional Intelligence Scale, the Organizational Justice questionnaire and the Utrecht Work Engagement Scale-9. We analysed the data using structural equation modelling.



Emotional intelligence and organisational justice were significant predictors and they accounted for 44% of the variance in nurses' work engagement. Bootstrap estimation confirmed an indirect effect of emotional intelligence on work engagement via organisational justice.



Emotional intelligence and organisational justice positively predict work engagement and organisational justice partially mediates the relationship between emotional intelligence and work engagement.


 TO CLINICAL PRACTICE
Our study supports the idea that enhancing organisational justice can increase the impact of emotional intelligence. Managers should take into account the importance of emotional intelligence and perceptions of organisational justice in human resources management and apply targeted interventions to foster work engagement.
A pilot study of the DBT coach: an interactive mobile phone application for individuals with borderline personality disorder and substance use disorder.

Dialectical behavior therapy (DBT) has received strong empirical support and is practiced widely as a treatment for borderline personality disorder (BPD) and BPD with comorbid substance use disorders (BPD-SUD). Therapeutic success in DBT requires that individuals generalize newly acquired skills to their natural environment. However, there have been only a limited number of options available to achieve this end. The primary goal of this research was to develop and test the feasibility of the DBT Coach, a software application for a smartphone, designed specifically to enhance generalization of a specific DBT skill (opposite action) among individuals with BPD-SUD. We conducted a quasiexperimental study in which 22 individuals who were enrolled in DBT treatment programs received a smartphone with the DBT Coach for 10 to 14 days and were instructed to use it as needed. Participants used the DBT Coach an average of nearly 15 times and gave high ratings of helpfulness and usability. Results indicate that both emotion intensity and urges to use substances significantly decreased within each coaching session. Furthermore, over the trial period, participants reported a decrease in depression and general distress. Mobile technology offering in vivo skills coaching may be a useful tool for reducing urges to use substances and engage in other maladaptive behavior by directly teaching and coaching in alternative, adaptive coping behavior.
BiDAF Model for Question Answering

Over the years, many companies and research groups have invested many resources towards the development of question answering systems because of their latent potential in understanding unstructured text. A very active part of the field of artificial intelligence, these systems seek to capture the semantic and syntactic relationships between words as they are used in modern language. In our study of cutting-edge question answering models, our group has sought to recreate Bidirectional Attention Flow, one of the most successful models in recent years. In this project, we implement a bidirectional attention flow neural network to answer questions in the SQuAD dataset. We encode the question and corresponding context paragraph using GloVe word embeddings, compute an attention matrix, and decode to find the answer to the question in the context paragraph. Our approach is largely modeled off that described in Bi-Directional Attention Flow for Machine Comprehension (Minjoon Seo et al). We believe we have implemented a good model, but our results at this time are less good than we would hope. We will describe these results, as well as our methodology, possible flaws, and improvements we hope to make in the future.
Efficient Multiplier Architectures for Galois Fields GF(2 4n)

This contribution introduces a new class of multipliers for finite fields GF((2 n ) 4 ). The architecture is based on a modified version of the Karatsuba-Ofman algorithm (KOA). By determining optimized field polynomials of degree four, the last stage of the KOA and the modulo reduction can be combined. This saves computation and area in VLSI implementations. The new algorithm leads to architectures which show a considerably improved gate complexity compared to traditional approaches and reduced delay if compared with KOA-based architectures with separate modulo reduction. The new multipliers lead to highly modular architectures and are, thus, well suited for VLSI implementations. Three types of field polynomials are introduced and conditions for their existence are established. For the small fields, where n = 2, 3, o, 8, which are of primary technical interest, optimized field polynomials were determined by an exhaustive search. For each field order, exact space and time complexities are provided. Index Terms —Galois fields, composite fields, multiplication, Karatsuba Ofman, modulo reduction, bit parallel, VLSI architecture. —————————— ✦ ——————————
Growth and morphological development of laboratory-reared larval and juvenile Pangasianodon hypophthalmus

The morphological development, including the fins, body proportions and pigmentation, of laboratory-reared larval and juvenile Pangasianodon hypophthalmus was described and their behavioral features were observed under rearing conditions. Body lengths (BL) of larvae and juveniles were 3.0 ± 0.2 (mean ± SD) mm just after hatching, and 12.9 ± 1.1 mm on day 13, reaching 23.4 ± 1.8 mm on day 25 after hatching. Aggregate fin ray numbers (for caudal fin, principal soft ray number) attained their full complements in specimens larger than 12.8 mm BL. Notochord flexion began in yolksac larvae on day 0 (10.5 h after hatching), with teeth buds and barbels appearing with jaw formation in yolksac flexion larvae on day 1. Melanophores on the body increased with growth, with a broad vertical band forming on the lateral line and an oblique band extending from above the pectoral fin base towards the forepart of the anal fin during the postflexion larval and juvenile stages. Body proportions became relatively constant in juveniles, except for maxillary barbel length (MBL), which continued to decrease. Yolksac flexion larvae started feeding on day 2 with the onset of intense cannibalism. Yolks were completely absorbed by day 3, and cannibalism ended by day 6. Subsequently, fish displayed a schooling behavior with growth, preferring relatively dark areas during the juvenile stage.
Incremental Clustering of Mobile Objects

Moving objects are becoming increasingly attractive to the data mining community due to continuous advances in technologies like GPS, mobile computers, and wireless communication devices. Mining spatio-temporal data can benefit many different functions: marketing team managers for identifying the right customers at the right time, cellular companies for optimizing the resources allocation, web site administrators for data allocation matters, animal migration researchers for understanding migration patterns, and meteorology experts for weather forecasting. In this research we use a compact representation of a mobile trajectory and define a new similarity measure between trajectories. We also propose an incremental clustering algorithm for finding evolving groups of similar mobile objects in spatio-temporal data. The algorithm is evaluated empirically by the quality of object clusters (using Dunn and Rand indexes), memory space efficiency, execution times, and scalability (run time vs. number of objects).
Reducing gender bias in word embeddings

Word embedding is a popular framework that represents text data as vectors of real numbers. These vectors capture semantics in language, and are used in a variety of natural language processing and machine learning applications. Despite these useful properties, word embeddings derived from ordinary language corpora necessarily exhibit human biases . We measure direct and indirect gender bias for occupation word vectors produced by the GloVe word embedding algorithm , then modify this algorithm to produce an embedding with less bias to mitigate amplifying the bias in downstream applications utilizing this embedding.