nils-herrmann/clean_scirepeval huggingface dataset:
Social network analysis and mining to support the assessment of on-line student participation

There is a growing number of courses delivered using elearning environments and their online discussions play an important role in collaborative learning of students. Even in courses with a few number of students, there could be thousands of messages generated in a few months within these forums. Manually evaluating the participation of students in such case is a significant challenge, considering the fact that current e-learning environments do not provide much information regarding the structure of interactions between students. There is a recent line of research on applying social network analysis (SNA) techniques to study these interactions.
 Here we propose to exploit SNA techniques, including community mining, in order to discover relevant structures in social networks we generate from student communications but also information networks we produce from the content of the exchanged messages. With visualization of these discovered relevant structures and the automated identification of central and peripheral participants, an instructor is provided with better means to assess participation in the online discussions. We implemented these new ideas in a toolbox, named Meerkat-ED, which automatically discovers relevant network structures, visualizes overall snapshots of interactions between the participants in the discussion forums, and outlines the leader/peripheral students. Moreover, it creates a hierarchical summarization of the discussed topics, which gives the instructor a quick view of what is under discussion. We believe exploiting the mining abilities of this toolbox would facilitate fair evaluation of students' participation in online courses.
Aquaculture: a newly emergent food production sector—and perspectives of its impacts on biodiversity and conservation

The fisheries sector in the course of the last three decades have been transformed from a developed country to a developing country dominance. Aquaculture, the farming of waters, though a millennia old tradition during this period has become a significant contributor to food fish production, currently accounting for nearly 50 % of global food fish consumption; in effect transforming our dependence from a hunted to a farmed supply as for all our staple food types. Aquaculture and indeed the fisheries sector as a whole is predominated in the developing countries, and accordingly the development strategies adopted by the sector are influenced by this. Aquaculture also being a newly emerged food production sector has being subjected to an increased level of public scrutiny, and one of the most contentious aspects has been its impacts on biodiversity. In this synthesis an attempt is made to assess the impacts of aquaculture on biodiversity. Instances of major impacts on biodiversity conservation arising from aquaculture, such as land use, effluent discharge, effects on wild populations, alien species among others are highlighted and critically examined. The influence of paradigm changes in development strategies and modern day market forces have begun to impact on aquaculture developments. Consequently, improvements in practices and adoption of more environmentally friendly approaches that have a decreasing negative influence on biodiversity conservation are highlighted. An attempt is also made to demonstrate direct and or indirect benefits of aquaculture, such as through being a substitute to meet human needs for food, particularly over-exploited and vulnerable fish stocks, and for other purposes (e.g. medicinal ingredients), on biodiversity conservation, often a neglected entity.
Hip disarticulation – case series analysis and literature review☆

To present a retrospective study of 16 patients submitted to hip disarticulation.



During the period of 16 years, 16 patients who underwent hip disarticulation were identified. All of them were studied based on clinical records regarding the gender, age at surgery, disarticulation cause, postoperative complications, mortality rates and functional status after hip disarticulation.



Hip disarticulation was performed electively in most cases and urgently in only three cases. The indications had the following origins: infection (n = 6), tumor (n = 6), trauma (n = 3), and ischemia (n = 2). The mean post-surgery survival was 200.5 days. The survival rates were 6875% after six months, 5625% after one year, and 50% after three years. The mortality rates were higher in disarticulations with traumatic (66.7%) and tumoral (60%) causes. Regarding the eight patients who survived, half of them ambulate with crutches and without prosthesis, 25% walk with limb prosthesis, and 25% are bedridden. Complications and mortality were higher in the cases of urgent surgery, and in those with traumatic and tumoral causes.



Hip disarticulation is a major ablative surgery with obvious implications for limb functionality, as well as high rates of complications and mortality. However, when performed at the correct time and with proper indication, this procedure can be life-saving and can ensure the return to the home environment with a certain degree of quality of life.
Music Retrieval and Recommendation: A Tutorial Overview

In this tutorial, we give an introduction to the field of and state of the art in music information retrieval (MIR). The tutorial particularly spotlights the question of music similarity, which is an essential aspect in music retrieval and recommendation. Three factors play a central role in MIR research: (1) the music content, i.e., the audio signal itself, (2) the music context, i.e., metadata in the widest sense, and (3) the listeners and their contexts, manifested in user-music interaction traces. We review approaches that extract features from all three data sources and combinations thereof and show how these features can be used for (large-scale) music indexing, music description, music similarity measurement, and recommendation. These methods are further showcased in a number of popular music applications, such as automatic playlist generation and personalized radio stationing, location-aware music recommendation, music search engines, and intelligent browsing interfaces. Additionally, related topics such as music identification, automatic music accompaniment and score following, and search and retrieval in the music production domain are discussed.
Drugs in Development for Influenza

The emergence and global spread of the 2009 pandemic H1N1 influenza virus reminds us that we are limited in the strategies available to control influenza infection. Vaccines are the best option for the prophylaxis and control of a pandemic; however, the lag time between virus identification and vaccine distribution exceeds 6 months and concerns regarding vaccine safety are a growing issue leading to vaccination refusal. In the short-term, antiviral therapy is vital to control the spread of influenza. However, we are currently limited to four licensed anti-influenza drugs: the neuraminidase inhibitors oseltamivir and zanamivir, and the M2 ion-channel inhibitors amantadine and rimantadine. The value of neuraminidase inhibitors was clearly established during the initial phases of the 2009 pandemic when vaccines were not available, i.e. stockpiles of antivirals are valuable. Unfortunately, as drug-resistant variants continue to emerge naturally and through selective pressure applied by use of antiviral drugs, the efficacy of these drugs declines. Because we cannot predict the strain of influenza virus that will cause the next epidemic or pandemic, it is important that we develop novel anti-influenza drugs with broad reactivity against all strains and subtypes, and consider moving to multiple drug therapy in the future. In this article we review the experimental data on investigational antiviral agents undergoing clinical trials (parenteral zanamivir and peramivir, long-acting neuraminidase inhibitors and the polymerase inhibitor favipiravir [T-705]) and experimental antiviral agents that target either the virus (the haemagglutinin inhibitor cyanovirin-N and thiazolides) or the host (fusion protein inhibitors [DAS181], cyclo-oxygenase-2 inhibitors and peroxisome proliferator-activated receptor agonists).
Deep Fluids: A Generative Network for Parameterized Fluid Simulations

This paper presents a novel generative model to synthesize fluid simulations from a set of reduced parameters. A convolutional neural network is trained on a collection of discrete, parameterizable fluid simulation velocity fields. Due to the capability of deep learning architectures to learn representative features of the data, our generative model is able to accurately approximate the training data set, while providing plausible interpolated in-betweens. The proposed generative model is optimized for fluids by a novel loss function that guarantees divergence-free velocity fields at all times. In addition, we demonstrate that we can handle complex parameterizations in reduced spaces, and advance simulations in time by integrating in the latent space with a second network. Our method models a wide variety of fluid behaviors, thus enabling applications such as fast construction of simulations, interpolation of fluids with different parameters, time re-sampling, latent space simulations, and compression of fluid simulation data. Reconstructed velocity fields are generated up to 700× faster than re-simulating the data with the underlying CPU solver, while achieving compression rates of up to 1300×. CCS Concepts • Computing methodologies → Physical simulation; Neural networks;
Breast enlargement in newborns: a folkloric-medical dilemma.

A male infant was brought to hospital aged eight weeks. He was born at full term via normal vaginal home delivery without any complications. The delivery was conducted by a traditional birth attendant and Apgar scores at birth were unrecorded. One week after the birth, the parents noticed an increase in size of the baby’s breasts. In accordance with cultural practice, they massaged the breasts in order to express milk, hoping that by doing so the size of the breasts would return to normal. However, the size of the breasts increased. They also reported that milk was being discharged spontaneously through the nipples. There was no history of drug intake neither by the mother nor the baby. The infant appeared clinically well and showed no signs of irritability. On examination, bilateral breast enlargement was observed of approximate diameter 6 cm. No tenderness, purulent discharge or any sign of inflammation were observed . Systemic and genital examination were unremarkable. Routine blood investigations were normal. Firm advice was given not to massage the breasts of the baby.
Adaptive Neural Trees

Deep neural networks and decision trees operate on largely separate paradigms; typically, the former performs representation learning with prespecified architectures, while the latter is characterised by learning hierarchies over pre-specified features with data-driven architectures. We unite the two via adaptive neural trees (ANTs), a model that incorporates representation learning into edges, routing functions and leaf nodes of a decision tree, along with a backpropagationbased training algorithm that adaptively grows the architecture from primitive modules (e.g., convolutional layers). We demonstrate that, whilst achieving competitive performance on classification and regression datasets, ANTs benefit from (i) lightweight inference via conditional computation, (ii) hierarchical separation of features useful to the predictive task e.g. learning meaningful class associations, such as separating natural vs. man-made objects, and (iii) a mechanism to adapt the architecture to the size and complexity of the training dataset.
Improved OCR based automatic vehicle number plate recognition using features trained neural network

Significant research and development of algorithms in intelligent transportation has grabbed more attention in recent years. An automated, fast, accurate and robust vehicle plate recognition system has become need for traffic control and law enforcement of traffic regulations; and the solution is ANPR. This paper is dedicated on an improved technique of OCR based license plate recognition using neural network trained dataset of object features. A blended algorithm for recognition of license plate is proposed and is compared with existing methods for improve accuracy. The whole system can be categorized under three major modules, namely License Plate Localization, Plate Character Segmentation, and Plate Character Recognition. The system is simulated on 300 national and international motor vehicle LP images and results obtained justifies the main requirement.
Resolver-to-Digital Converter for Compensation of Amplitude Imbalances using DQ Transformation

Resolvers are transducers that are used to sense the angular position of rotational machines. The analog resolver is necessary to use resolver to digital converter. Among the RDC software method, angle tracking observer (ATO) is the most popular method. In an actual resolverbased position sensing system, amplitude imbalance dominantly distorts the estimate position information of ATO. Minority papers have reported position error compensation of resolver’s output signal with amplitude imbalance. This paper proposes new ATO algorithm in order to compensate position errors caused by the amplitude imbalance. There is no need premeasured off line data. This is easy, simple, cost-effective, and able to work on line compensation. To verify feasibility of the proposed algorithm, simulation and experiments are carried out.
Efficient Patch-Wise Semantic Segmentation for Large-Scale Remote Sensing Images

Efficient and accurate semantic segmentation is the key technique for automatic remote sensing image analysis. While there have been many segmentation methods based on traditional hand-craft feature extractors, it is still challenging to process high-resolution and large-scale remote sensing images. In this work, a novel patch-wise semantic segmentation method with a new training strategy based on fully convolutional networks is presented to segment common land resources. First, to handle the high-resolution image, the images are split as local patches and then a patch-wise network is built. Second, training data is preprocessed in several ways to meet the specific characteristics of remote sensing images, i.e., color imbalance, object rotation variations and lens distortion. Third, a multi-scale training strategy is developed to solve the severe scale variation problem. In addition, the impact of conditional random field (CRF) is studied to improve the precision. The proposed method was evaluated on a dataset collected from a capital city in West China with the Gaofen-2 satellite. The dataset contains ten common land resources (Grassland, Road, etc.). The experimental results show that the proposed algorithm achieves 54.96% in terms of mean intersection over union (MIoU) and outperforms other state-of-the-art methods in remote sensing image segmentation.
Towards Understanding the Influence of a Virtual Agent ’ s Emotional Expression on Personal Space

The concept of personal space is a key element of social interactions. As such, it is a recurring subject of investigations in the context of research on proxemics. Using virtual-reality-based experiments, we contribute to this area by evaluating the direct effects of emotional expressions of an approaching virtual agent on an individual’s behavioral and physiological responses. As a pilot study focusing on the emotion expressed solely by facial expressions gave promising results, we now present a study design to gain more insight.
Improved 8-Point Approximate DCT for Image and Video Compression Requiring Only 14 Additions

Video processing systems such as HEVC requiring low energy consumption needed for the multimedia market has lead to extensive development in fast algorithms for the efficient approximation of 2-D DCT transforms. The DCT is employed in a multitude of compression standards due to its remarkable energy compaction properties. Multiplier-free approximate DCT transforms have been proposed that offer superior compression performance at very low circuit complexity. Such approximations can be realized in digital VLSI hardware using additions and subtractions only, leading to significant reductions in chip area and power consumption compared to conventional DCTs and integer transforms. In this paper, we introduce a novel 8-point DCT approximation that requires only 14 addition operations and no multiplications. The proposed transform possesses low computational complexity and is compared to state-of-the-art DCT approximations in terms of both algorithm complexity and peak signal-to-noise ratio. The proposed DCT approximation is a candidate for reconfigurable video standards such as HEVC. The proposed transform and several other DCT approximations are mapped to systolic-array digital architectures and physically realized as digital prototype circuits using FPGA technology and mapped to 45 nm CMOS technology.
Fuzzy Information Retrieval

Information retrieval used to mean looking through thousands of strings of texts to find words or symbols that matched a user’s query. Today, there are many models that help index and search more effectively so retrieval takes a lot less time. Information retrieval (IR) is often seen as a subfield of computer science and shares some modeling, applications, storage applications and techniques, as do other disciplines like artificial intelligence, database management, and parallel computing. This book introduces the topic of IR and how it differs from other computer science disciplines. A discussion of the history of modern IR is briefly presented, and the notation of IR as used in this book is defined. The complex notation of relevance is discussed. Some applications of IR is noted as well since IR has many practical uses today. Using information retrieval with fuzzy logic to search for software terms can help find software components and ultimately help increase the reuse of software. This is just one practical application of IR that is covered in this book. Some of the classical models of IR is presented as a contrast to extending the Boolean model. This includes a brief mention of the source of weights for the various models. In a typical retrieval environment, answers are either yes or no, i.e., on or off. On the other hand, fuzzy logic can bring in a “degree of ” match, vs. a crisp, i.e., strict match. This, too, is looked at and explored in much detail, showing how it can be applied to information retrieval. Fuzzy logic is often times considered a soft computing application and this book explores how IR with fuzzy logic and its membership functions as weights can help indexing, querying, and matching. Since fuzzy set theory and logic is explored in IR systems, the explanation of where the fuzz is ensues. The concept of relevance feedback, including pseudorelevance feedback is explored for the various models of IR. For the extended Boolean model, the use of genetic algorithms for relevance feedback is delved into. The concept of query expansion is explored using rough set theory. Various term relationships is modeled and presented, and the model extended for fuzzy retrieval. An example using the UMLS terms is also presented. The model is also extended for term relationships beyond synonyms. Finally, this book looks at clustering, both crisp and fuzzy, to see how that can improve retrieval performance. An example is presented to illustrate the concepts.
Job satisfaction theories : Traceability to employee performance in organizations

Job satisfaction and employee performance has been a topic of research for decades. Whether job satisfaction influences employee satisfaction in organizations remains a crucial issue to managers and psychologists. That is where the problem lies. Therefore, the objective of this paper is to trace the relationship between job satisfaction and employee performance in organizations with particular reference to Nigeria. Related literature on the some theories of job satisfaction such as affective events, two-factor, equity and job characteristics was reviewed and findings from these theories indicate that a number of factors like achievement, recognition, responsibility, pay, work conditions and so on, have positive influence on employee performance in organizations. The paper adds to the theoretical debate on whether job satisfaction impacts positively on employee performance. It concludes that though the concept of job satisfaction is complex, using appropriate variables and mechanisms can go a long way in enhancing employee performance. It recommends that managers should use those factors that impact employee performance to make them happy, better their well being and the environment. It further specifies appropriate mechanisms using a theoretical approach to support empirical approaches which often lack clarity as to why the variables are related.
Using the Kalman Filter to track Human Interactive Motion - Modelling and Initialization of the Kalm

Based on an example of translational motion, this report shows how to model and initialize the Kalman Filter. Basic rules about physical motion are introduced to point out, that the well-known laws of physical motion are a mere approximation. Hence, motion of non-constant velocity or acceleration is modelled by additional use of white noise. Special attention is drawn to the matrix initialization for use in the Kalman Filter, as, in general, papers and books do not give any hint on this; thus inducing the impression that initializing is not important and may be arbitrary. For unknown matrices many users of the Kalman Filter choose the unity matrix. Sometimes it works, sometimes it does not. In order to close this gap, initialization is shown on the example of human interactive motion. In contrast to measuring instruments with documented measurement errors in manuals, the errors generated by vision-based sensoring must be estimated carefully. Of course, the described methods may be adapted to other circumstances.
Induced pluripotent stem cells for regenerative medicine.

With the discovery of induced pluripotent stem (iPS) cells, it is now possible to convert differentiated somatic cells into multipotent stem cells that have the capacity to generate all cell types of adult tissues. Thus, there is a wide variety of applications for this technology, including regenerative medicine, in vitro disease modeling, and drug screening/discovery. Although biological and biochemical techniques have been well established for cell reprogramming, bioengineering technologies offer novel tools for the reprogramming, expansion, isolation, and differentiation of iPS cells. In this article, we review these bioengineering approaches for the derivation and manipulation of iPS cells and focus on their relevance to regenerative medicine.
Implementation of object-relational DBMSs in a relational database course

Object-relational DBMS was gradually added as a new topic to the author's database course in response to the rapid changes in DBMS technology in the real world. Implementation of ORDBMS technology in a traditional relational database course had significant impacts on the database curriculum. As an outcome, students were able to solve problems that could not be solved well in a relational database. ORDBMS was implemented with Universal Modeling Language (UML) and the Oracle 8i server. Course design, teaching methodology, class activities and the outcome of the course are discussed.