nils-herrmann/clean_scirepeval huggingface dataset:
Encoding Source Language with Convolutional Neural Network for Machine Translation

The recently proposed neural network joint model (NNJM) (Devlin et al., 2014) augments the n-gram target language model with a heuristically chosen source context window, achieving state-of-the-art performance in SMT. In this paper, we give a more systematic treatment by summarizing the relevant source information through a convolutional architecture guided by the target information. With different guiding signals during decoding, our specifically designed convolution+gating architectures can pinpoint the parts of a source sentence that are relevant to predicting a target word, and fuse them with the context of entire source sentence to form a unified representation. This representation, together with target language words, are fed to a deep neural network (DNN) to form a stronger NNJM. Experiments on two NIST Chinese-English translation tasks show that the proposed model can achieve significant improvements over the previous NNJM by up to +1.01 BLEU points on average.
Biopreservation, an ecological approach to improve the safety and shelf-life of foods

Biopreservation, defined as the extension of shelf life and enhanced safety of foods by the use of natural or controlled microbiota and/or antimicrobial compounds, is an innocuous and ecological approach to the problem of food preservation and has gained increasing attention in recent years. Consequently, certain lactic acid bacteria (LAB), with demonstrated antimicrobial properties commonly associated with foods, are being assayed to increase the safety and/or prolong the shelf life of foods. The antagonistic properties of LAB derive from competition for nutrients and the production of one or more antimicrobial active metabolites such as organic acids (lactic and acetic), hydrogen peroxide, and antimicrobial peptides (bacteriocins). Nowadays the use of LAB bacteriocins is considered an integral part of hurdle technology. Their combined use allows most pathogenic and spoilage bacteria to be controlled and also extend their inhibitory activity spectrum to such intrinsically resistant organisms as the Gram-negative bacteria.
Psychosexual outcome of gender-dysphoric children.

To establish the psychosexual outcome of gender-dysphoric children at 16 years or older and to examine childhood characteristics related to psychosexual outcome.



We studied 77 children who had been referred in childhood to our clinic because of gender dysphoria (59 boys, 18 girls; mean age 8.4 years, age range 5-12 years). In childhood, we measured the children's cross-gender identification and discomfort with their own sex and gender roles. At follow-up 10.4 +/- 3.4 years later, 54 children (mean age 18.9 years, age range 16-28 years) agreed to participate. In this group, we assessed gender dysphoria and sexual orientation.



At follow-up, 30% of the 77 participants (19 boys and 4 girls) did not respond to our recruiting letter or were not traceable; 27% (12 boys and 9 girls) were still gender dysphoric (persistence group), and 43% (desistance group: 28 boys and 5 girls) were no longer gender dysphoric. Both boys and girls in the persistence group were more extremely cross-gendered in behavior and feelings and were more likely to fulfill gender identity disorder (GID) criteria in childhood than the children in the other two groups. At follow-up, nearly all male and female participants in the persistence group reported having a homosexual or bisexual sexual orientation. In the desistance group, all of the girls and half of the boys reported having a heterosexual orientation. The other half of the boys in the desistance group had a homosexual or bisexual sexual orientation.



Most children with gender dysphoria will not remain gender dysphoric after puberty. Children with persistent GID are characterized by more extreme gender dysphoria in childhood than children with desisting gender dysphoria. With regard to sexual orientation, the most likely outcome of childhood GID is homosexuality or bisexuality.
Design and implementation of vehicle tracking system using GPS/GSM/GPRS technology and smartphone application

An efficient vehicle tracking system is designed and implemented for tracking the movement of any equipped vehicle from any location at any time. The proposed system made good use of a popular technology that combines a Smartphone application with a microcontroller. This will be easy to make and inexpensive compared to others. The designed in-vehicle device works using Global Positioning System (GPS) and Global system for mobile communication / General Packet Radio Service (GSM/GPRS) technology that is one of the most common ways for vehicle tracking. The device is embedded inside a vehicle whose position is to be determined and tracked in real-time. A microcontroller is used to control the GPS and GSM/GPRS modules. The vehicle tracking system uses the GPS module to get geographic coordinates at regular time intervals. The GSM/GPRS module is used to transmit and update the vehicle location to a database. A Smartphone application is also developed for continuously monitoring the vehicle location. The Google Maps API is used to display the vehicle on the map in the Smartphone application. Thus, users will be able to continuously monitor a moving vehicle on demand using the Smartphone application and determine the estimated distance and time for the vehicle to arrive at a given destination. In order to show the feasibility and effectiveness of the system, this paper presents experimental results of the vehicle tracking system and some experiences on practical implementations.
Novel Feature Extraction, Selection and Fusion for Effective Malware Family Classification

Modern malware is designed with mutation characteristics, namely polymorphism and metamorphism, which causes an enormous growth in the number of variants of malware samples. Categorization of malware samples on the basis of their behaviors is essential for the computer security community, because they receive huge number of malware everyday, and the signature extraction process is usually based on malicious parts characterizing malware families. Microsoft released a malware classification challenge in 2015 with a huge dataset of near 0.5 terabytes of data, containing more than 20K malware samples. The analysis of this dataset inspired the development of a novel paradigm that is effective in categorizing malware variants into their actual family groups. This paradigm is presented and discussed in the present paper, where emphasis has been given to the phases related to the extraction, and selection of a set of novel features for the effective representation of malware samples. Features can be grouped according to different characteristics of malware behavior, and their fusion is performed according to a per-class weighting paradigm. The proposed method achieved a very high accuracy ($\approx$ 0.998) on the Microsoft Malware Challenge dataset.
Distant Supervision for Relation Extraction with Sentence-Level Attention and Entity Descriptions

Distant supervision for relation extraction is an efficient method to scale relation extraction to very large corpora which contains thousands of relations. However, the existing approaches have flaws on selecting valid instances and lack of background knowledge about the entities. In this paper, we propose a sentence-level attention model to select the valid instances, which makes full use of the supervision information from knowledge bases. And we extract entity descriptions from Freebase and Wikipedia pages to supplement background knowledge for our task. The background knowledge not only provides more information for predicting relations, but also brings better entity representations for the attention module. We conduct three experiments on a widely used dataset and the experimental results show that our approach outperforms all the baseline systems significantly.
The effect of testing versus restudy on retention: a meta-analytic review of the testing effect.

Engaging in a test over previously studied information can serve as a potent learning event, a phenomenon referred to as the testing effect. Despite a surge of research in the past decade, existing theories have not yet provided a cohesive account of testing phenomena. The present study uses meta-analysis to examine the effects of testing versus restudy on retention. Key results indicate support for the role of effortful processing as a contributor to the testing effect, with initial recall tests yielding larger testing benefits than recognition tests. Limited support was found for existing theoretical accounts attributing the testing effect to enhanced semantic elaboration, indicating that consideration of alternative mechanisms is warranted in explaining testing effects. Future theoretical accounts of the testing effect may benefit from consideration of episodic and contextually derived contributions to retention resulting from memory retrieval. Additionally, the bifurcation model of the testing effect is considered as a viable framework from which to characterize the patterns of results present across the literature.
Machine Learning Meets Databases

Machine Learning has become highly popular due to several success stories in data-driven applications. Prominent examples include object detection in images, speech recognition, and text translation. According to Gartner’s 2016 Hype Cycle for Emerging Technologies, Machine Learning is currently at its peak of inflated expectations, with several other application domains trying to exploit the use of Machine Learning technology. Since data-driven applications are a fundamental cornerstone of the database community as well, it becomes natural to ask how these fields relate to each other. In this article, we will therefore provide a brief introduction to the field of Machine Learning, we will discuss its interplay with other fields such as Data Mining and Databases, and we provide an overview of recent data management systems integrating Machine Learning functionality.
GAN Q-learning

Distributional reinforcement learning (distributional RL) has seen empirical success in complex Markov Decision Processes (MDPs) in the setting of nonlinear function approximation. However there are many different ways in which one can leverage the distributional approach to reinforcement learning. In this paper, we propose GAN Q-learning, a novel distributional RL method based on generative adversarial networks (GANs) and analyze its performance in simple tabular environments, as well as OpenAI Gym. We empirically show that our algorithm leverages the flexibility and blackbox approach of deep learning models while providing a viable alternative to traditional methods.
A Survey on the Performance Analysis of WT , PF , EMD & EEMD Methods used in ECG Signal Processing

A noiseless ECG identification technology is an emerging new biometric modality. Different techniques for de-noising of ECG signal are prevalent in recent literatures such as Particle Filter (PF), wavelet transforms (WT), Empirical Mode Decomposition (EMD) & Ensemble-EMD Method. In view of the fact that Analysis of ECG signals becomes difficult to inspect the cardiac activity in the presence of Noise signals. So, de-noising of ECG signal is extremely important to prevent misinterpretation of patient’s cardiac activity which can lead to wrong diagnosis and further to death. This paper is surveying a comparison on the performance of the Particle Filter (PF), wavelet transforms (WT) and Empirical Mode Decomposition (EMD) & Ensemble EMD methods in context of denoising of an electrocardiogram (ECG) signal. Analysis of the paper provides us the way of selecting best de-noising technique of ECG signal based on the numerical value in terms of SNR and RMSE. The study is limited to signals corrupted by additive white Gaussian random noise.
Learning Attentional Communication for Multi-Agent Cooperation

Communication could potentially be an effective way for multi-agent cooperation. However, information sharing among all agents or in predefined communication architectures that existing methods adopt can be problematic. When there is a large number of agents, agents cannot differentiate valuable information that helps cooperative decision making from globally shared information. Therefore, communication barely helps, and could even impair the learning of multi-agent cooperation. Predefined communication architectures, on the other hand, restrict communication among agents and thus restrain potential cooperation. To tackle these difficulties, in this paper, we propose an attentional communication model that learns when communication is needed and how to integrate shared information for cooperative decision making. Our model leads to efficient and effective communication for large-scale multi-agent cooperation. Empirically, we show the strength of our model in a variety of cooperative scenarios, where agents are able to develop more coordinated and sophisticated strategies than existing methods.
Combining Words and Speech Prosody for Automatic Topic Segmentation

We present a probabilistic model that uses both prosodic and lexical cues for the automatic segmentation of speech into topic units. The approach combines hidden Markov models, statistical la ngu ge models, and prosody-based decision trees. Lexical informa tion is obtained from a speech recognizer, and prosodic features ar e extracted automatically from speech waveforms. We evaluate o ur approach on the Broadcast News corpus, using standard evaluat ion metrics. Results show that the prosodic model alone outperf orms the word-based segmentation method. Furthermore, we achie ve an additional reduction in error by combining the prosodic and wordbased knowledge sources.
A hovering control strategy for a tail-sitter VTOL UAV that increases stability against large disturbance

The application range of UAVs (unmanned aerial vehicles) is expanding along with performance upgrades. Vertical take-off and landing (VTOL) aircraft has the merits of both fixed-wing and rotary-wing aircraft. Tail-sitting is the simplest way for the VTOL maneuver since it does not need extra actuators. However, conventional hovering control for a tail-sitter UAV is not robust enough against large disturbance such as a blast of wind, a bird strike, and so on. It is experimentally observed that the conventional quaternion feedback hovering control often fails to keep stability when the control compensates large attitude errors. This paper proposes a novel hovering control strategy for a tail-sitter VTOL UAV that increases stability against large disturbance. In order to verify the proposed hovering control strategy, simulations and experiments on hovering of the UAV are performed giving large attitude errors. The results show that the proposed control strategy successfully compensates initial large attitude errors keeping stability, while the conventional quaternion feedback controller fails.
Face Recognition Algorithms

The goal of this ”proyecto fin de carrera” was to produce a review of the face detection and face recognition literature as comprehensive as possible. Face detection was included as a unavoidable preprocessing step for face recogntion, and as an issue by itself, because it presents its own difficulties and challenges, sometimes quite different from face recognition. We have soon recognized that the amount of published information is unmanageable for a short term effort, such as required of a PFC, so in agreement with the supervisor we have stopped at a reasonable time, having reviewed most conventional face detection and face recognition approaches, leaving advanced issues, such as video face recognition or expression invariances, for the future work in the framework of a doctoral research. I have tried to gather much of the mathematical foundations of the approaches reviewed aiming for a self contained work, which is, of course, rather difficult to produce. My supervisor encouraged me to follow formalism as close as possible, preparing this PFC report more like an academic report than an engineering project report.
Reproductive biology of Paracentrotus lividus ( Echinodermata , Echinoidea ) in two contrasting habitats of northern Tunisia ( southeast Mediterranean ) Running head : P . lividus reproduction in Northern Tunisia

The reproductive strategies of the sea urchin, Paracentrotus lividus, was studied in the Bay of Tunis. Samples were collected monthly, from September 1993 to August 1995, in two sites which differ in their marine vegetation and their exposure to wave action. Histological examination demonstrated a cycle of gametogenesis with six reproductive stages and a main breeding period occurring between April and June. Gonad indices varied between sites and years, the sheltered site presenting a higher investment in reproduction. This difference was essentially induced by the largest sea urchins (above 40 mm in diameter). Repletion indices showed a clear pattern without difference between sites and years. The sea urchin increase in feeding activity was controlled by the need to allocate nutrient to the gonad during the mature stage. But the gonad investment was not correlated with the intensity of food intake. Hydrodynamic conditions might play a key role in diverting energy to the maintenance in an exposed environment at the expense of reproduction.
Strategy as Action: From Porter to Anti-Porter

In the post-crisis era new concepts are emerging, while some old and dysfunctional ones are being discarded. Strategy is about making series of decisions that drive corporate action under specific coupling with company’s environment and context. Because decisions are actions, so the strategy itself is action, not just a description of action. In the area of traditional strategy, descriptions (information) have replaced action (knowledge), talk has replaced walk. We start from the premise that strategy is what company does, and what company does is its strategy. One cannot run a company just on descriptions and framed mission statements. The role of customers is crucial: the customer shapes strategy and triggers corporate action. Without respecting the customer there is no viable strategy. This is why we label the action-based strategic thinking as „Anti-Porter“: consumers do not want tradeoffs and so truly effective strategy cannot be rooted in Porter’s tradeoffs.
Process Mining in Healthcare

Healthcare costs have increased dramatically and the demand for highquality care will only grow in our aging society. At the same time,more event data are being collected about care processes. Healthcare Information Systems (HIS) have hundreds of tables with patient-related event data. Therefore, it is quite natural to exploit these data to improve care processes while reducing costs. Data science techniqueswill play a crucial role in this endeavor. Processmining can be used to improve compliance and performance while reducing costs. The chapter sets the scene for process mining in healthcare, thus serving as an introduction to this SpringerBrief.
Organic agriculture and ecological justice : ethics and practice

Scope and purpose of the chapter....................................................................79 Sustainability, globalization and organic agriculture ..........................................79 Dimensions of sustainability ...........................................................................80 Different meanings of globalization and sustainability...................................82 Sustainability and organic agriculture.............................................................83 The ethics and justice of ecological justice .........................................................84 Ecological justice as an ethical concept ..........................................................85 The justice of ecological justice ......................................................................87 Summing up ....................................................................................................89 Challenges for organic agriculture ......................................................................90 Commodification of commons........................................................................91 How to address externalities ...........................................................................92 Growing distances...........................................................................................94 Putting ecological justice into organic practice...................................................97 The way of certified organic agriculture .........................................................98 The way of non-certified organic agriculture................................................102 Organic agriculture as an alternative example ..............................................106 Conclusions.......................................................................................................108
GPU-Aware MPI on RDMA-Enabled Clusters: Design, Implementation and Evaluation

Designing high-performance and scalable applications on GPU clusters requires tackling several challenges. The key challenge is the separate host memory and device memory, which requires programmers to use multiple programming models, such as CUDA and MPI, to operate on data in different memory spaces. This challenge becomes more difficult to tackle when non-contiguous data in multidimensional structures is used by real-world applications. These challenges limit the programming productivity and the application performance. We propose the GPU-Aware MPI to support data communication from GPU to GPU using standard MPI. It unifies the separate memory spaces, and avoids explicit CPU-GPU data movement and CPU/GPU buffer management. It supports all MPI datatypes on device memory with two algorithms: a GPU datatype vectorization algorithm and a vector based GPU kernel data pack and unpack algorithm. A pipeline is designed to overlap the non-contiguous data packing and unpacking on GPUs, the data movement on the PCIe, and the RDMA data transfer on the network. We incorporate our design with the open-source MPI library MVAPICH2 and optimize a production application: the multiphase 3D LBM. Besides the increase of programming productivity, we observe up to 19.9 percent improvement in application-level performance on 64 GPUs of the Oakley supercomputer.