nils-herrmann/clean_scirepeval huggingface dataset:
Determining the Effects of LMS Learning Behaviors on Academic Achievement in a Learning Analytic Perspective

Two of the most important outcomes of learning analytics are predicting students’ learning and providing effective feedback. Learning Management Systems (LMS), which are widely used to support online and face-to-face learning, provide extensive research opportunities with detailed records of background data regarding users’ behaviors. The purpose of this study was to investigate the effects of undergraduate students’ LMS learning behaviors on their academic achievements. In line with this purpose, the participating students’ online learning behaviors in LMS were examined by using learning analytics for 14 weeks, and the relationship between students’ behaviors and their academic achievements was analyzed, followed by an analysis of their views about the influence of LMS on their academic achievement. The present study, in which quantitative and qualitative data were collected, was carried out with the explanatory mixed method. A total of 71 undergraduate students participated in the study. The results revealed that the students used LMSs as a support to face-to-face education more intensively on course days (at the beginning of the related lessons and at nights on course days) and that they activated the content elements the most. Lastly, almost all the students agreed that LMSs helped increase their academic achievement only when LMSs included such features as effectiveness, interaction, reinforcement, attractive design, social media support, and accessibility.
Learning Latent Representations of 3D Human Pose with Deep Neural Networks

Most recent approaches to monocular 3D pose estimation rely on Deep Learning. They either train a Convolutional Neural Network to directly regress from an image to a 3D pose, which ignores the dependencies between human joints, or model these dependencies via a max-margin structured learning framework, which involves a high computational cost at inference time. In this paper, we introduce a Deep Learning regression architecture for structured prediction of 3D human pose from monocular images or 2D joint location heatmaps that relies on an overcomplete autoencoder to learn a high-dimensional latent pose representation and accounts for joint dependencies. We further propose an efficient Long Short-Term Memory network to enforce temporal consistency on 3D pose predictions. We demonstrate that our approach achieves state-of-the-art performance both in terms of structure preservation and prediction accuracy on standard 3D human pose estimation benchmarks.
GOSELO: Goal-Directed Obstacle and Self-Location Map for Robot Navigation Using Reactive Neural Networks

Robot navigation using deep neural networks has been drawing a great deal of attention. Although reactive neural networks easily learn expert behaviors and are computationally efficient, they suffer from generalization of policies learned in specific environments. As such, reinforcement learning and value iteration approaches for learning generalized policies have been proposed. However, these approaches are more costly. In this letter, we tackle the problem of learning reactive neural networks that are applicable to general environments. The key concept is to crop, rotate, and rescale an obstacle map according to the goal location and the agent's current location so that the map representation will be better correlated with self-movement in the general navigation task, rather than the layout of the environment. Furthermore, in addition to the obstacle map, we input a map of visited locations that contains the movement history of the agent, in order to avoid failures that the agent travels back and forth repeatedly over the same location. Experimental results reveal that the proposed network outperforms the state-of-the-art value iteration network in the grid-world navigation task. We also demonstrate that the proposed model can be well generalized to unseen obstacles and unknown terrain. Finally, we demonstrate that the proposed system enables a mobile robot to successfully navigate in a real dynamic environment.
ISSUES IN AUDIOVISUAL TRANSLATION

Audiovisual translation is one of several overlapping umbrella terms that include ‘media translation’, ‘multimedia translation’, ‘multimodal translation’ and ‘screen translation’. These different terms all set out to cover the interlingual transfer of verbal language when it is transmitted and accessed both visually and acoustically, usually, but not necessarily, through some kind of electronic device. Theatrical plays and opera, for example, are clearly audiovisual yet, until recently, audiences required no technological devices to access their translations; actors and singers simply acted and sang the translated versions. Nowadays, however, opera is frequently performed in the original language with surtitles in the target language projected on to the stage. Furthermore, electronic librettos placed on the back of each seat containing translations are now becoming widely available. However, to date most research in audiovisual translation has been dedicated to the field of screen translation, which, while being both audiovisual and multimedial in nature, is specifically understood to refer to the translation of films and other products for cinema, TV, video and DVD. After the introduction of the first talking pictures in the 1920s a solution needed to be found to allow films to circulate despite language barriers. How to translate film dialogues and make movie-going accessible to speakers of all languages was to become a major concern for both North American and European film directors. Today, of course, screens are no longer restricted to cinema theatres alone. Television screens, computer screens and a series of devices such as DVD players, video game consoles, GPS navigation devices and mobile phones are also able to send out audiovisual products to be translated into scores of languages. Hence, strictly speaking, screen translation includes translations for any electronic appliance with a screen; however, for the purposes of this chapter, the term will be used mainly to refer to translations for the most popular products, namely for cinema, TV, video and DVD, and videogames. The two most widespread modalities adopted for translating products for the screen are dubbing and subtitling.1 Dubbing is a process which uses the acoustic channel for translational purposes, while subtitling is visual and involves a written translation that is superimposed on to the
Risk analysis method : FMEA / FMECA in the organizations

This is a report on the FMEA/FMECA risk analysis method in industries. We have visited at Parker Hannifin, Borås to know their techniques to implement it and found that the company is familiar with Design and Process FMEA only and organization’s FMEA software is based on MS Excel sheet to put all of the data’s of FMEA team’s risk analysis investigation. The company follows a limit of RPN’s 200 and any value beyond this limit and equal to this is marked red. The software presents a graph of RPN’s of before action taken and after action taken. The industry is not so familiar with FMECA but using qualitative part of criticality analysis (criticality matrix of severity on Y-axis and occurrence on Xaxis). The report explains about differences of FMEA and FMECA. The company is making risk analysis if they are asked to do so by the top management. It is helping the company to avoid accident, re-design and making a reliable design or process.
Automatic multi-document Arabic text summarization using clustering and keyphrase extraction

Automatic text summarization has become important due to the rapid growth of information texts since it is very difficult for human beings to manually summarize large documents of texts. A full understanding of the document is essential to form an ideal summary. However, achieving full understanding is either difficult or impossible for computers. Therefore, selecting important sentences from the original text and presenting these sentences as a summary present the most common techniques in automated text summarization. Arabic natural language processing lacks tools and resources which are essential to advance research in Arabic text summarization. In addition to the limited resources, there has been little attention and research done in this field. Arabic text summarization still suffer from low accuracy as they use simple summarization techniques. The aim of this research is to improve Arabic text summarization by using clustering and keyphrase extraction. This study proposes a combined clustering method to group Arabic documents into several clusters. Keyphrase extraction module is applied to extract important keyphrases from each cluster, which helps to identify the most important sentences and find similar sentences based on several similarity algorithms. These algorithms are applied to extract one sentence from a group of similar sentences while ignoring the other similar sentences. The Recall-Oriented Understudy for Gisting Evaluation (ROGUE) metrics were used for the evaluation. For the summarization dataset the corpus DUC2002 was used. This model achieved an accuracy of 43.4%. The experiments have proved that the proposed model has given better performance in comparison to other work.
A Single Attention-Based Combination of CNN and RNN for Relation Classification

As a vital task in natural language processing, relation classification aims to identify relation types between entities from texts. In this paper, we propose a novel Att-RCNN model to extract text features and classify relations by combining recurrent neural network (RNN) and convolutional neural network (CNN). This network structure utilizes RNN to extract higher level contextual representations of words and CNN to obtain sentence features for the relation classification task. In addition to this network structure, both word-level and sentence-level attention mechanisms are employed in Att-RCNN to strengthen critical words and features to promote the model performance. Moreover, we conduct experiments on four distinct datasets: SemEval-2010 task 8, SemEval-2018 task 7 (two subtask datasets), and KBP37 dataset. Compared with the previous public models, Att-RCNN has the overall best performance and achieves the highest  $F_{1}$  score, especially on the KBP37 dataset.
On-line Building Energy Optimization using Deep Reinforcement Learning

Unprecedented high volumes of data are becoming available with the growth of the advanced metering infrastructure. These are expected to benefit planning and operation of the future power system, and to help the customers transition from a passive to an active role. In this paper, we explore for the first time in the smart grid context the benefits of using Deep Reinforcement Learning, a hybrid type of methods that combines Reinforcement Learning with Deep Learning, to perform on-line optimization of schedules for building energy management systems. The learning procedure was explored using two methods, Deep Q-learning and Deep Policy Gradient, both of them being extended to perform multiple actions simultaneously. The proposed approach was validated on the large-scale Pecan Street Inc. database. This highly-dimensional database includes information about photovoltaic power generation, electric vehicles as well as buildings appliances. Moreover, these on-line energy scheduling strategies could be used to provide realtime feedback to consumers to encourage more efficient use of electricity.
Biopharmaceutics Classification System: The Scientific Basis for Biowaiver Extensions

The current BSC guidance issued by the FDA allows for biowaivers based on conservative criteria. Possible new criteria and class boundaries are proposed for additional biowaivers based on the underlying physiology of the gastrointestinal tract. The proposed changes in new class boundaries for solubility and permeability are as follows: 1. Narrow the required solubility pH range from 1.0-7.5 to 1.0-6.8. 2. Reduce the high permeability requirement from 90% to 85%. The following new criterion and potential biowaiver extension require more research: 1. Define a new intermediate permeability class boundary. 2. Allow biowaivers for highly soluble and intermediately permeable drugs in IR solid oral dosage forms with no less than 85% dissolved in 15 min in all physiologically relevant dissolution media, provided these IR products contain only known excipients that do not affect the oral drug absorption. The following areas require more extensive research: 1. Increase the dose volume for solubility classification to 500 mL. 2. Include bile salt in the solubility measurement. 3. Use the intrinsic dissolution method for solubility classification. 4. Define an intermediate solubility class for BCS Class II drugs. 5. Include surfactants in in vitro dissolution testing.
Combination of Cloud Computing and Internet of Things (IOT) in Medical Monitoring Systems

With the fast development of cloud computing and computer science technology, the combination of the IOT and clod computing in the medical-assisted environment is urgently needed. The prior research focus more on individual development of the single technique, quite a less research on the field of medical monitoring and managing service application have been conducted. Therefore, in this paper, we study and analyze the application of cloud computing and the Internet of Things on the field of medical environment. We are trying to make the combination of the two kinds of technology monitoring and management information system in hospital. Remote monitoring cloud platform architecture model (RMCPHI) set up medical information in the first place. Then the RMCPHI architecture was analyzed. Eventually, the last effective PSOSAA algorithm proposed the hospital medical information service cloud system monitoring and management application. Experimental simulation illustrates that the proposed algorithm outperforms the other state-of-the-art algorithms. Further potential research areas are discussed.
Catching the Temporal Regions-of-Interest for Video Captioning

As a crucial challenge for video understanding, exploiting the spatial-temporal structure of video has attracted much attention recently, especially on video captioning. Inspired by the insight that people always focus on certain interested regions of video content, we propose a novel approach which will automatically focus on regions-of-interest and catch their temporal structures. In our approach, we utilize a specific attention model to adaptively select regions-of-interest for each video frame. Then a Dual Memory Recurrent Model (DMRM) is introduced to incorporate temporal structure of global features and regions-of-interest features in parallel, which will obtain rough understanding of video content and particular information of regions-of-interest. Since the attention model could not always catch the right interests, we additionally adopt semantic supervision to attend to interested regions more correctly. We evaluate our method for video captioning on two public benchmarks: the Microsoft Video Description Corpus (MSVD) and the Montreal Video Annotation Dataset (M-VAD). The experiments demonstrate that catching temporal regions-of-interest information really enhances the representation of input videos and our approach obtains the state-of-the-art results on popular evaluation metrics like BLEU-4, CIDEr, and METEOR.
Music as a memory enhancer in patients with Alzheimer's disease

Musical mnemonics have a long and diverse history of popular use. In addition, music processing in general is often considered spared by the neurodegenerative effects of Alzheimer's disease (AD). Research examining these two phenomena is limited, and no work to our knowledge has explored the effectiveness of musical mnemonics in AD. The present study sought to investigate the effect of music at encoding on the subsequent recognition of associated verbal information. Lyrics of unfamiliar children's songs were presented bimodally at encoding, and visual stimuli were accompanied by either a sung or a spoken recording. Patients with AD demonstrated better recognition accuracy for the sung lyrics than the spoken lyrics, while healthy older adults showed no significant difference between the two conditions. We propose two possible explanations for these findings: first, that the brain areas subserving music processing may be preferentially spared by AD, allowing a more holistic encoding that facilitates recognition, and second, that music heightens arousal in patients with AD, allowing better attention and improved memory.
Large-scale vehicle re-identification in urban surveillance videos

Vehicle, as a significant object class in urban surveillance, attracts massive focuses in computer vision field, such as detection, tracking, and classification. Among them, vehicle re-identification (Re-Id) is an important yet frontier topic, which not only faces the challenges of enormous intra-class and subtle inter-class differences of vehicles in multicameras, but also suffers from the complicated environments in urban surveillance scenarios. Besides, the existing vehicle related datasets all neglect the requirements of vehicle Re-Id: 1) massive vehicles captured in real-world traffic environment; and 2) applicable recurrence rate to give cross-camera vehicle search for vehicle Re-Id. To facilitate vehicle Re-Id research, we propose a large-scale benchmark dataset for vehicle Re-Id in the real-world urban surveillance scenario, named “VeRi”. It contains over 40,000 bounding boxes of 619 vehicles captured by 20 cameras in unconstrained traffic scene. Moreover, each vehicle is captured by 2~18 cameras in different viewpoints, illuminations, and resolutions to provide high recurrence rate for vehicle Re-Id. Finally, we evaluate six competitive vehicle Re-Id methods on VeRi and propose a baseline which combines the color, texture, and highlevel semantic information extracted by deep neural network.
AUTOMATIC PLANT PEST DETECTION AND RECOGNITION USING k-MEANS CLUSTERING ALGORITHM AND CORRESPONDENCE FILTERS

Plant pest recognition and detection is vital for f od security, quality of life and a stable agricult ural economy. This research demonstrates the combination of the k -m ans clustering algorithm and the correspondence filter to achieve pest detection and recognition. The detecti on of the dataset is achieved by partitioning the d ata space into Voronoi cells, which tends to find clusters of comparable spatial extents, thereby separating the obj cts (pests) from the background (pest habitat). The det ction is established by extracting the variant dis inctive attributes between the pest and its habitat (leaf, stem) and using the correspondence filter to identi fy the plant pests to obtain correlation peak values for differe nt datasets. This work further establishes that the recognition probability from the pest image is directly proport i nal to the height of the output signal and invers ely proportional to the viewing angles, which further c onfirmed that the recognition of plant pests is a f unction of their position and viewing angle. It is encouraging to note that the correspondence filter can achieve rotational invariance of pests up to angles of 360 degrees, wh ich proves the effectiveness of the algorithm for t he detection and recognition of plant pests.
Two collaborative filtering recommender systems based on sparse dictionary coding

This paper proposes two types of recommender systems based on sparse dictionary coding. Firstly, a novel predictive recommender system that attempts to predict a user’s future rating of a specific item. Secondly, a top-n recommender system which finds a list of items predicted to be most relevant for a given user. The proposed methods are assessed using a variety of different metrics and are shown to be competitive with existing collaborative filtering recommender systems. Specifically, the sparse dictionary-based predictive recommender has advantages over existing methods in terms of a lower computational cost and not requiring parameter tuning. The sparse dictionary-based top-n recommender system has advantages over existing methods in terms of the accuracy of the predictions it makes and not requiring parameter tuning. An open-source software implemented and used for the evaluation in this paper is also provided for reproducibility.
Deep Almond : A Deep Learning-based Virtual Assistant [ Language-to-code synthesis of Trigger-Action programs using Seq 2 Seq Neural Networks ]

Virtual assistants are the cutting edge of end user interaction, thanks to endless set of capabilities across multiple services. The natural language techniques thus need to be evolved to match the level of power and sophistication that users expect from virtual assistants. In this report we investigate an existing deep learning model for semantic parsing, and we apply it to the problem of converting natural language to trigger-action programs for the Almond virtual assistant. We implement a one layer seq2seq model with attention layer, and experiment with grammar constraints and different RNN cells. We take advantage of its existing dataset and we experiment with different ways to extend the training set. Our parser shows mixed results on the different Almond test sets, performing better than the state of the art on synthetic benchmarks by about 10% but poorer on realistic user data by about 15%. Furthermore, our parser is shown to be extensible to generalization, as well as or better than the current system employed by Almond.
Brain Structure and Response to Emotional Stimuli as Related to Gut Microbial Profiles in Healthy Women.

Brain-gut-microbiota interactions may play an important role in human health and behavior. Although rodent models have demonstrated effects of the gut microbiota on emotional, nociceptive, and social behaviors, there is little translational human evidence to date. In this study, we identify brain and behavioral characteristics of healthy women clustered by gut microbiota profiles.



Forty women supplied fecal samples for 16S rRNA profiling. Microbial clusters were identified using Partitioning Around Medoids. Functional magnetic resonance imaging was acquired. Microbiota-based group differences were analyzed in response to affective images. Structural and diffusion tensor imaging provided gray matter metrics (volume, cortical thickness, mean curvature, surface area) as well as fiber density between regions. A sparse Partial Least Square-Discrimination Analysis was applied to discriminate microbiota clusters using white and gray matter metrics.



Two bacterial genus-based clusters were identified, one with greater Bacteroides abundance (n = 33) and one with greater Prevotella abundance (n = 7). The Prevotella group showed less hippocampal activity viewing negative valences images. White and gray matter imaging discriminated the two clusters, with accuracy of 66.7% and 87.2%, respectively. The Prevotella cluster was associated with differences in emotional, attentional, and sensory processing regions. For gray matter, the Bacteroides cluster showed greater prominence in the cerebellum, frontal regions, and the hippocampus.



These results support the concept of brain-gut-microbiota interactions in healthy humans. Further examination of the interaction between gut microbes, brain, and affect in humans is needed to inform preclinical reports that microbial modulation may affect mood and behavior.