nils-herrmann/clean_scirepeval huggingface dataset:
To appear in ICB 2018 Longitudinal Study of Child Face Recognition

We present a longitudinal study of face recognition performance on Children Longitudinal Face (CLF) dataset containing 3, 682 face images of 919 subjects, in the age group [2, 18] years. Each subject has at least four face images acquired over a time span of up to six years. Face comparison scores are obtained from (i) a state-of-the-art COTS matcher (COTS-A), (ii) an open-source matcher (FaceNet), and (iii) a simple sum fusion of scores obtained from COTSA and FaceNet matchers. To improve the performance of the open-source FaceNet matcher for child face recognition, we were able to fine-tune it on an independent training set of 3,294 face images of 1,119 children in the age group [3, 18] years. Multilevel statistical models are fit to genuine comparison scores from the CLF dataset to determine the decrease in face recognition accuracy over time. Additionally, we analyze both the verification and open-set identification accuracies in order to evaluate state-of-the-art face recognition technology for tracing and identifying children lost at a young age as victims of child trafficking or abduction.
An empirical study of the mechanisms of mindfulness in a mindfulness-based stress reduction program.

S. L. Shapiro and colleagues (2006) have described a testable theory of the mechanisms of mindfulness and how it affects positive change. They describe a model in which mindfulness training leads to a fundamental change in relationship to experience (reperceiving), which leads to changes in self-regulation, values clarification, cognitive and behavioral flexibility, and exposure. These four variables, in turn, result in salutogenic outcomes. Analyses of responses from participants in a mindfulness-based stress-reduction program did not support the mediating effect of changes in reperceiving on the relationship of mindfulness with those four variables. However, when mindfulness and reperceiving scores were combined, partial support was found for the mediating effect of the four variables on measures of psychological distress. Issues arising in attempts to test the proposed theory are discussed, including the description of the model variables and the challenges to their assessment.
Mastering Digital Transformation: the Path of a Financial Services Provider towards a Digital Transformation Strategy

To master the challenges of a digital transformation and to systematically address IT’s multifaceted transformative impacts on an organization’s inner and outer environments, top management is increasingly formulating and implementing a digital transformation strategy (DTS). To date, there have been few details of DTS formation concerning its underlying processes and activities. In this study, an interpretive case study approach is employed and DTS formation is investigated from a process/activity perspective. By using an activity-based process model that builds on IS strategizing, an in-depth case study at a large financial services provider was conducted. The results show that this DTS was predominantly shaped by a diversity of emergent strategizing activities through a bottom-up process and prior to the introduction of a holistic approach by top management. Top management then sought to formalize emergent strategy contents by formulating and implementing a DTS that comprised a shared target picture, distinct digital transformation governance, and measures to increase the share of deliberate strategy contents. Besides providing practical implications for DTS formulation and implementation, this study contributes to the literature on digital transformation, IS strategy, and IS strategizing.
Cooperative (rather than autonomous) vehicle-highway automation systems

The recent DARPA-sponsored automated vehicle "Challenges" have generated strong interest in both the research community and the general public, raising consciousness about the possibilities for vehicle automation. Driverless vehicles make good subjects for the visually-oriented media, and they pose enough interesting research challenges to occupy generations of graduate students. However, automated vehicles also have the potential to help solve a variety of real-world problems. Engineers need to think carefully about which of those problems we are actually solving.A well-engineered system should be designed to satisfy specific needs, and those needs should be reflected in the definition of system requirements. Alternative technological approaches can then be evaluated and traded off based on their ability to meet the requirements. The article describes the rather different needs of the public road transportation system and the military transportation system, and then shows how those needs influence the requirements for automated vehicle systems. These requirements point toward significantly different technical approaches, but it is possible to find some limited areas of technical commonality.
Thoraco-omphalopagus conjoined twins in Chamois-coloured domestic goat kids.

Conjoined twins have been observed in a wide range of mammalian and non-mammalian species; they are considered to be more common in bovine, less frequent in sheep and pig and extremely rare in horse and goat. A pair of female conjoined twins was delivered from a 2-year-old Chamois-coloured domestic goat. Post-mortem examination revealed two identical and symmetrical twins, fused from the manubrium sterni to the region just caudal to the umbilicus. The rib cages were conjoined in the ventral plane with a single set of pericardial, pleural and peritoneal cavities. Internal examination revealed the presence of a common diaphragm and a single enlarged liver. Within a single central pericardium, two malformed hearts were present. Reports on this type of congenital duplication in goats have not been found in the literature. Thoracopagus and thoraco-omphalopagus are the most common types of conjoined twins in human beings and are associated with the highest mortality because of the frequent incidence of complex cardiac anatomy.
Dynamic Evolutionary Algorithm With Variable Relocation

Many real-world optimization problems have to be solved under the presence of uncertainties. A significant number of these uncertainty problems falls into the dynamic optimization category in which the fitness function varies through time. For this class of problems, an evolutionary algorithm is expected to perform satisfactorily in spite of different degrees and frequencies of change in the fitness landscape. In addition, the dynamic evolutionary algorithm should warrant an acceptable performance improvement to justify the additional computational cost. Effective reuse of previous evolutionary information is a must as it facilitates a faster convergence after a change has occurred. This paper proposes a new dynamic evolutionary algorithm that uses variable relocation to adapt already converged or currently evolving individuals to the new environmental condition. The proposed algorithm relocates those individuals based on their change in function value due to the change in the environment and the average sensitivities of their decision variables to the corresponding change in the objective space. The relocation occurs during the transient stage of the evolutionary process, and the algorithm reuses as much information as possible from the previous evolutionary history. As a result, the algorithm shows improved adaptation and convergence. The newly adapted population is shown to be fitter to the new environment than the original or most randomly generated population. The algorithm has been tested by several dynamic benchmark problems and has shown competitive results compared to some chosen state-of-the-art dynamic evolutionary approaches.
A New Gradient Based Character Segmentation Method for Video Text Recognition

The current OCR cannot segment words and characters from video images due to complex background as well as low resolution of video images. To have better accuracy, this paper presents a new gradient based method for words and character segmentation from text line of any orientation in video frames for recognition. We propose a Max-Min clustering concept to obtain text cluster from the normalized absolute gradient feature matrix of the video text line image. Union of the text cluster with the output of Canny operation of the input video text line is proposed to restore missing text candidates. Then a run length algorithm is applied on the text candidate image for identifying word gaps. We propose a new idea for segmenting characters from the restored word image based on the fact that the text height difference at the character boundary column is smaller than that of the other columns of the word image. We have conducted experiments on a large dataset at two levels (word and character level) in terms of recall, precision and f-measure. Our experimental setup involves 3527 characters of English and Chinese, and this dataset is selected from TRECVID database of 2005 and 2006.
Learning to Remember Translation History with a Continuous Cache

Existing neural machine translation (NMT) models generally translate sentences in isolation, missing the opportunity to take advantage of document-level information. In this work, we propose to augment NMT models with a very light-weight cache-like memory network, which stores recent hidden representations as translation history. The probability distribution over generated words is updated online depending on the translation history retrieved from the memory, endowing NMT models with the capability to dynamically adapt over time. Experiments on multiple domains with different topics and styles show the effectiveness of the proposed approach with negligible impact on the computational cost.
Analog transistor models of bacterial genetic circuits

We show that compact analog current-mode circuits are effective at quantitatively modeling the behavior of genetic circuits. We present experimental biological data from genetic activator (PBAD) and repressor (PlacO) promoter circuits in E. coli. Simple subthreshold cascaded-differential-pair transistor circuits have input-output characteristics that quantitatively represent this data. Such foundational analog circuits can provide efficient conceptual, modeling, and simulation tools for the design and analysis of circuits in synthetic and systems biology.
Testing Incremental Difficulty Design in Platformer Games

Designing difficulty levels in platformer games is a challenge for game designers. It is important because design decisions that affect difficulty also directly affect player experience. Consequently, design strategies for balancing game difficulty are discussed by both academics and game designers. In this paper, we study how manipulating the following design decisions, commonly found in platformers, moderates difficulty: Scroll Speed, Target Size, Jump Task Complexity, and Perspective. Results for Scroll Speed and Target Size indicate that errors increase as speed increases and platform size decreases. However, results for jump task complexity demonstrate a separation of errors from task complexity. Specifically, while double-jump tasks are harder than single-jump tasks, triple-jump tasks appear to be as difficult as double-jump tasks. Additionally, the study demonstrates how changes in perspective affect the errors made by players in gameplay. The study results are applicable both to automatic level generation and dynamic difficulty adjustment in platformer games.
Designing the Future of Personal Fashion

Advances in computer vision and machine learning are changing the way people dress and buy clothes. Given the vast space of fashion problems, where can data-driven technologies provide the most value? To understand consumer pain points and opportunities for technological interventions, this paper presents the results from two independent need-finding studies that explore the gold-standard of personalized shopping: interacting with a personal stylist. Through interviews with five personal stylists, we study the range of problems they address and their in-person processes for working with clients. In a separate study, we investigate how styling experiences map to online settings by building and releasing a chatbot that connects users to one-on-one sessions with a stylist, acquiring more than 70 organic users in three weeks. These conversations reveal that in-person and online styling sessions share similar goals, but online sessions often involve smaller problems that can be resolved more quickly. Based on these explorations, we propose future highly personalized, online interactions that address consumer trust and uncertainty, and discuss opportunities for automation.
Generalized stacking of layerwise-trained Deep Convolutional Neural Networks for document image classification

This article presents our recent study of a lightweight Deep Convolutional Neural Network (DCNN) architecture for document image classification. Here, we concentrated on training of a committee of generalized, compact and powerful base DCNNs. A support vector machine (SVM) is used to combine the outputs of individual DCNNs. The main novelty of the present study is introduction of supervised layerwise training of DCNN architecture in document classification tasks for better initialization of weights of individual DCNNs. Each DCNN of the committee is trained for a specific part or the whole document. Also, here we used the principle of generalized stacking for combining the normalized outputs of all the members of the DCNN committee. The proposed document classification strategy has been tested on the well-known Tobacco3482 document image dataset. Results of our experimentations show that the proposed strategy involving a considerably smaller network architecture can produce comparable document classification accuracies in competition with the state-of-the-art architectures making it more suitable for use in comparatively low configuration mobile devices.
ARGoS: a modular, parallel, multi-engine simulator for multi-robot systems

We present a novel multi-robot simulator named ARGoS. ARGoS is designed to simulate complex experiments involving large swarms of robots of different types. ARGoS is the first multi-robot simulator that is at the same time both efficient (fast performance with many robots) and flexible (highly customizable for specific experiments). Novel design choices in ARGoS have enabled this breakthrough. First, in ARGoS, it is possible to partition the simulated space into multiple sub-spaces, managed by different physics engines running in parallel. Second, ARGoS’ architecture is multi-threaded, thus designed to optimize the usage of modern multi-core CPUs. Finally, the architecture of ARGoS is highly modular, enabling easy addition of custom features and appropriate allocation of computational resources. We assess the efficiency of ARGoS and showcase its flexibility with targeted experiments. Experimental results demonstrate that simulation run-time increases linearly with the number of robots. A 2D-dynamics simulation of 10,000 e-puck robots can be performed in 60 % of the time taken by the corresponding real-world experiment. We show how ARGoS can be extended to suit the needs of an experiment in which custom functionality is necessary to achieve sufficient simulation accuracy.
A Unified Virtual Power Decoupling Method for Droop-Controlled Parallel Inverters in Microgrids

The coupling between the active and the reactive power of the droop-controlled inverter is critical in the low-voltage microgrid. The conventional virtual power-based control strategy can decouple the active and the reactive power of the inverter, whereas the active power of the load is probably not equally shared among the parallel inverters in the microgrid. To tackle this issue, this paper proposes an improved virtual power-based control method for the droop-controlled parallel inverters with a unified rotation angle in the power transformation. Meanwhile, the optimal value of the unified rotation angle is rigorously derived through investigating the relative stability of the system by innovatively adopting Routh Stability Criterion. Moreover, the proposed method is enhanced by introducing low-pass filters in the coupling path for further mitigating the coupling between the active and the reactive power of the inverter. Additionally, the stability analysis and parameter design for the proposed method are comprehensively presented. Finally, the effectiveness of the proposed method is validated by the simulation and experimental results.
Increasing emitter efficiency in 3.3-kV enhanced trench IGBTs for higher short-circuit capability

In this paper, a 3.3-kV Enhanced Trench IGBT has been designed with a high emitter efficiency, for improving its short-circuit robustness. The carrier distribution profile has been shaped in a way that it is possible to increase the electric field at the surface of the IGBT, and thereby, counteract the Kirk Effect onset. This design approach is beneficial for mitigating high-frequency oscillations, typically observed in IGBTs under short-circuit conditions. The effectiveness of the proposed design rule is validated by means of mixed-mode device simulations. Then, two IGBTs have been fabricated with different emitter efficiencies and tested under short circuit, validating that the high-frequency oscillations can be mitigated, with higher emitter efficiency IGBT designs.
A Low-Power 32-Channel Digitally Programmable Neural Recording Integrated Circuit

We report the design of an ultra-low-power 32-channel neural-recording integrated circuit (chip) in a 0.18 μ m CMOS technology. The chip consists of eight neural recording modules where each module contains four neural amplifiers, an analog multiplexer, an A/D converter, and a serial programming interface. Each amplifier can be programmed to record either spikes or LFPs with a programmable gain from 49-66 dB. To minimize the total power consumption, an adaptive-biasing scheme is utilized to adjust each amplifier's input-referred noise to suit the background noise at the recording site. The amplifier's input-referred noise can be adjusted from 11.2 μVrms (total power of 5.4 μW) down to 5.4 μVrms (total power of 20 μW) in the spike-recording setting. The ADC in each recording module digitizes the a.c. signal input to each amplifier at 8-bit precision with a sampling rate of 31.25 kS/s per channel, with an average power consumption of 483 nW per channel, and, because of a.c. coupling, allows d.c. operation over a wide dynamic range. It achieves an ENOB of 7.65, resulting in a net efficiency of 77 fJ/State, making it one of the most energy-efficient designs for neural recording applications. The presented chip was successfully tested in an in vivo wireless recording experiment from a behaving primate with an average power dissipation per channel of 10.1 μ W. The neural amplifier and the ADC occupy areas of 0.03 mm2 and 0.02 mm2 respectively, making our design simultaneously area efficient and power efficient, thus enabling scaling to high channel-count systems.
Analysis of importance of pre-processing in prediction of hypertension

Hypertension is a leading risk factor contributing to cardiovascular diseases and cardiovascular mortality and morbidity across the world. Developing good risk prediction models to arbitrate individuals risk predictions, pay off high value by reducing mortality and morbidity. To build such a good prediction model a reliable BP data is needed, but real-life data is often noisy, inconsistent and incomplete. Hence, it is important to pre-process data before building prediction model. This paper aims to pre-process (impute data) the data using unsupervised neural network, Adaptive Resonance Theory 2 (ART2) clusters and build prediction model using decision trees, Naive Bayes, random forest after handling imbalanced class of dataset from one of the Health and Demographic Surveillance System (HDSS) site Vadu, India. Its a part of International Network for the Demographic Evaluation of Populations and Their Health (INDEPTH) network in developing countries, aiming to help developing countries to set health priorities based on longitudinal evidence. The experimental results of the proposed technique shows the importance of pre-processing in enhancing the performance of the prediction models.
Direct Synthesis of Complex Loaded Chebyshev Filters in a Complex Filtering Network

In this paper, a direct synthesis approach is proposed for designing a Chebyshev filter that matches the frequency variant complex loads at both the ports. The approach is based on the power wave renormalization theory and two practical assumptions: 1) the prescribed transmission zeros are stationary and 2) the reflection zeros are located along an imaginary axis. Three conditions are derived to stipulate the characteristic polynomials of the filter's responses through the renormalization of reference load impedances. These conditions are sequentially applied to ensure that the filter is physically realizable and well matched to the complex loads. It has been shown that the optimally synthesized filtering network that consists of an ideal filter cascaded with a piece of transmission line of an optimal length at each port can match the complex loads over a given frequency band with the best effort. By incorporating with an adjustment of the junction parameters, the approach provides an analytical yet flexible way to synthesize advanced microwave circuits composed of multiple filters connected together through some generalized junctions. The effectiveness of the method is demonstrated through three synthesis examples.
Seaweed extract improve drought tolerance of soybean by regulating stress-response genes

There is an increasing global concern about the availability of water for agricultural use. Drought stress negatively impacts plant physiology and crop productivity. Soybean (Glycine max) is one of the important oilseed crops, and its productivity is often reduced by drought. In this study, a commercial extract of Ascophyllum nodosum (ANE) was evaluated for its potential to alleviate drought stress in soybean. The aim of this study was to determine the effects of ANE on the response of soybean plants to drought stress by monitoring stomatal conductance, relative leaf water content, antioxidant activity and expression of stress-responsive genes. Plants treated with ANE had higher relative water content and higher stomatal conductance under drought stress. During early recovery in the post-drought phase, ANE treated plants had significantly higher stomatal conductance. The antioxidant activity was also found higher in the plants treated with ANE. In addition, ANE-treatment led to changes in the expression of stress-responsive genes: GmCYP707A1a, GmCYP707A3b, GmRD22, GmRD20, GmDREB1B, GmERD1, GmNFYA3, FIB1a, GmPIP1b, GmGST, GmBIP and GmTp55. Taken together, these results suggest that applications of ANE improve the drought tolerance of soybean by changing physiology and gene expression.
SPL : Unit Testing Performance

Unit testing is an attractive quality management tool in the software development process, however, practical obstacles make it difficult to use unit tests for performance testing. We present Stochastic Performance Logic, a formalism for expressing performance requirements, together with interpretations that facilitate performance evaluation in the unit test context. The formalism and the interpretations are evaluated in multiple experiments, to demonstrate (1) the ability to reflect typical developer concerns related to performance, and (2) the ability to identify performance differences in realistic measurements. This work was partially supported by the EU project ASCENS 257414 and Charles University institutional funding D3S, Technical Report no. D3S-TR-2014-04