Miracle-dz/UniFAR-FTUs huggingface dataset:
Sentiment analysis on students' real-time feedback

Previous literature identifies that students’ real-time feedback is important in the learning process. There are numerous studies that have collected students’ feedback in real time. However, they include several limitations of which the most important is analysing the feedback. In this thesis, we address these limitations by proposing a system that will automatically analyse students’ feedback in real time and present the analysis results to the lecturer. To create such a system, we propose the use of sentiment analysis. The extensive literature highlights the importance of this research in the sentiment analysis field, as there is no research exploring sentiment analysis for students’ real-time feedback and research in the educational domain has been focused mainly on e-learning. The literature shows that emotions are also important in learning and could be detected using sentiment analysis. Consequently, this research is also important as there is little research in detecting emotion from students’ feedback. Polarity detection is explored and two experiments were done to identify the best combination of preprocessing, features and machine learning techniques to create an optimal model for polarity detection of students’ feedback. As a result, we found that the optimal model was to use a lower level of preprocessing, unigrams for features and Complement Naive Bayes for the classifier. Emotion detection in students’ feedback is also explored. Two experiments were done to find the optimal model to detect emotions related to learning from students’ feedback. The results showed that models which detect a single emotion have a better performance than multiple emotion models. Three emotions (i.e. Amused, Bored, and Excited) were more easily detected than others. The optimal model to detect emotion included a low level preprocessing, unigrams as features and Complement Naive Bayes as the classifier. Sarcasm detection in students’ feedback is explored. Our results showed that the lower level of preprocessing led to the best performance. Moreover, by adding other features, such as polarity and emotions, to the unigrams, sarcasm detection increased. Lastly, the best classifier to detect sarcasm was Complement Naive Bayes. Visualisation of the sentiment analysis models results is also investigated. Six visualisations were created and evaluated by lecturers. The findings indicated that there lecturers had no strong preference for a specific visualisation. The system evaluation was performed in real-life settings. As a result, we found that the lecturers were highly satisfied with the system, while the students had a neutral towards negative view of it.
Fabrication of graphene-based flexible devices utilizing a soft lithographic patterning method

There has been considerable interest in soft lithographic patterning processing of large scale graphene sheets due to the low cost and simplicity of the patterning process along with the exceptional electrical or physical properties of graphene. These properties include an extremely high carrier mobility and excellent mechanical strength. Recently, a study has reported that single layer graphene grown via chemical vapor deposition (CVD) was patterned and transferred to a target surface by controlling the surface energy of the polydimethylsiloxane (PDMS) stamp. However, applications are limited because of the challenge of CVD-graphene functionalization for devices such as chemical or bio-sensors. In addition, graphene-based layers patterned with a micron scale width on the surface of biocompatible silk fibroin thin films, which are not suitable for conventional CMOS processes such as the patterning or etching of substrates, have yet to be reported. Herein, we developed a soft lithographic patterning process via surface energy modification for advanced graphene-based flexible devices such as transistors or chemical sensors. Using this approach, the surface of a relief-patterned elastomeric stamp was functionalized with hydrophilic dimethylsulfoxide molecules to enhance the surface energy of the stamp and to remove the graphene-based layer from the initial substrate and transfer it to a target surface. As a proof of concept using this soft lithographic patterning technique, we demonstrated a simple and efficient chemical sensor consisting of reduced graphene oxide and a metallic nanoparticle composite. A flexible graphene-based device on a biocompatible silk fibroin substrate, which is attachable to an arbitrary target surface, was also successfully fabricated. Briefly, a soft lithographic patterning process via surface energy modification was developed for advanced graphene-based flexible devices such as transistors or chemical sensors and attachable devices on a biocompatible silk fibroin substrate. Significantly, this soft lithographic patterning technique enables us to demonstrate a simple and efficient chemical sensor based on reduced graphene oxide (rGO), a metallic nanoparticle composite, and an attachable graphene-based device on a silk fibroin thin film.
Witches, Druids and King Arthur

Illustrations Introduction Acknowledgements 1 How Myths are Made 2 Arthur and the Academics 3 Glastonbury: Alternative Histories 4 The New Old Paganism 5 Paganism in the Missing Countries 6 A Modest Look at Ritual Nudity 7 The Inklings and the Gods 8 The New Druidry 9 Living with Witchcraft Notes Index.
La chronographie du système d'Empédocle : addenda et corrigenda

Le present article est consacre a la reedition et, pour certaines d’entre elles, a l’edition de scholies byzantines comportant des donnees chiffrees sur les periodes du cycle d’Empedocle. Celles-ci ont ete copiees au xiie siecle sous forme de gloses a differents traites d’Aristote. On s’attachera a demontrer la coherence interne de ces nouveaux renseignements, leur compatibilite avec les fragments conserves du poeme Sur la nature et, enfin, on suggerera un canal possible de transmission.
Molecular and dissociative adsorption of multiple hydrogen molecules on transition metal decorated C 60

Recently we have predicted Phys. Rev. Lett. 94, 175501 2005 that Ti-decorated carbon nanotubes can adsorb up to 8-wt. % hydrogen at ambient conditions. Here we show that a similar phenomenon occurs in light transition-metal decorated C60. While Sc and Ti prefer the hexagon H sites with a binding energy of 2.1 eV, V and Cr prefer double-bond D sites with binding energies of 1.3 and 0.8 eV, respectively. Heavier metals such as Mn, Fe, and Co do not bond on C60. Once the metals are adsorbed on C60, each can bind up to four hydrogen molecules with an average binding energy of 0.3– 0.5 eV/ H2. At high metal coverage, we show that aC 60 can accommodate six D-site and eight H-site metals, which can adsorb up to 56 H2 molecules, corresponding to 7.5 wt. %.
Post-medieval archaeology in Britain

The archaeology of the Post-Medieval rural Landscape the archaeology of the post-Medieval town post-Medieval church archaeology fortifications the archaeology of ship-wrecks sources of power before the invention of the steam engine the archaeology of ferrous metals non-ferrous metals mining and quarrying other industries the archaeology of glass ceramics.
A Reliable Approach to Automatic Assessment of Short Answer Free Responses

This paper discusses an innovative approach to the computer assisted scoring of student responses in WebLAS (web-based language assessment system)- a language assessment system delivered entirely over the web. Expected student responses are limited production free response questions.The portions of WebLAS with which we are concerned are the task creation and scoring modules. Within the task creation module, instructors and language experts do not only provide the task input and prompt. More importantly, they interactively inform the system how and how much to score student responses. This interaction consists of WebLAS' natural language processing (NLP) modules searching for alternatives of the provided "gold standard" (Hirschman et al, 2000) answer and asking for confirmation of score assignment. WebLAS processes and stores all this information within its database, to be used in the task delivery and scoring phases.
A Comparative Analysis of Individual Communication Processes in Small Group Behavior between Homogeneous and Heterogeneous Groups

This study compares the communication behaviors (i.e., turn taking and speaking time distribution) of a cross-cultural small-group communication environment to further investigate the generalizability of Du-Babcock’s (1999) findings. The study also adapted Oeztel’s (1998) effective decision-making theoretical model (EDMT) to broaden the investigation to include cultural dimensions and group composition influences on communication behaviors in small group meetings. The findings suggest that although culture is a contributing factor, the influence of culture and group composition on the communication behaviors of individuals in a homogeneous or in a heterogeneous group decision-making meeting is mixed.
Synthesis of Rh Fv phage‐antibodies using VH and VL germline genes

Antibodies to the D antigen of the Rh system use a restricted set of immunoglobulin V and J gene segments, especially VH DP50 and DP63, JH6, Vλ DPL16 and Jλ 2/3. These gene segments may confer a natural affinity on the antibodies for the D antigen and this hypothesis has been tested by constructing two single‐chain Fv phage‐antibody libraries based on the germline gene segments DP50 and DP63; structural variability was obtained by insertion of 11 amino acids in random sequence in the VHCDR3. 10 anti‐D antibodies were selected from these libraries, each with a unique VHCDR3. In contrast, selections with the CcEe antigens produced antibodies reacting with the Rh polypeptide molecules but without strict blood group specificity. One of these latter DP50‐based antibodies was converted into 12 different antibodies with specificity for E by replacing the original germline light chain with chains from a rearranged L chain library. The CDR1 and CDR2 sequences of the DP50‐based antibodies were common to both anti‐D and anti‐E molecules; differentiation between D and E specificity was dependent on VHCDR3 sequences and their correct pairing with an appropriate L chain.
Detection of homozygous and heterozygous SMN deletions of spinal muscular atrophy in a single assay with multiplex ligation-dependent probe amplification.

OBJECTIVE Spinal muscular atrophy(SMA), an autosomal recessive neuromuscular degeneration of the anterior horn cells of the spinal cord and brain stem, results in one of the most common diseases with muscle fatigue and atrophy. Most SMA cases including all the types are due to the homozygous deletion of at least exon 7 within the survival motor neuron 1 (SMN-1) gene. Although a "golden standard" assay (PCR with mismatch primer followed by enzyme digestion) is very reliable for the identification of homozygous SMN-1 deletion, the carrier detection of heterozygous SMN-1 deletion remains a challenge. METHODS Some PCR-based gene dosage assays or multiplex PCR allow for the determination of the copy number of SMN-1 gene to identify heterozygous deletion, but these procedures are often time consuming and available on a limited clinical basis. Recently developed MLPA (multiplex ligation-dependent probe amplification) is an efficient procedure that can accurately analyze relative quantification to establish the copy number of the SMN gene. We performed a validation for simultaneous detection of homozygous SMN-1 deletions of SMA patients and heterozygous SMN-1 deletions of SMA carriers in a simple assay using a MLPA-SMA assay specific reagent. RESULTS Six out of 20 patients with SMA were found to have homozygous SMN-1 deletion, confirmed by the PCR/digestion assay. All 4 parents of the children with SMA had heterozygous SMN-1 deletion, confirmed by an independent relative quantitative analysis. CONCLUSION MLPA provides a simple, rapid and accurate method of simultaneously detecting homozygous deletions and heterozygous deletions in a single assay for both SMN-1 and SMN-2 genes.
Subband adaptive equalization of time-varying channels

The potential presence of fractional delays, nonminimum phase parts, and a colouring of the channel output can require very large adaptive equalizers exhibiting slow convergence for LMS-type algorithms. In this paper, we address the equalization problem via a subband approach, which can significantly reduce computational complexity and improve convergence speed. Of particular interest in this paper is the tracking behaviour, which determines the speed with which the algorithm is capable of following time variations in the channel. We give reasons why tracking of LMS-type subband adaptive algorithms should be comparable to the full-band case. Simulation results with a time varying channel are presented.
Smoothing-Norm Preconditioning for Regularizing Minimum-Residual Methods

When GMRES (or a similar minimum-residual algorithm such as RRGMRES, MINRES, or MR-II) is applied to a discrete ill-posed problem with a square matrix, in some cases the iterates can be considered as regularized solutions. We show how to precondition these methods in such a way that the iterations take into account a smoothing norm for the solution. This technique is well established for CGLS, but it does not immediately carry over to minimum-residual methods when the smoothing norm is a seminorm or a Sobolev norm. We develop a new technique which works for any smoothing norm of the form $\|L\,x\|_2$ and which preserves symmetry if the coefficient matrix is symmetric. We also discuss the efficient implementation of our preconditioning technique, and we demonstrate its performance with numerical examples in one and two dimensions.
Survey of Multi-Sensor Information Fusion Filtering and Control

Multi-sensor information fusion (MSIF) is an emerging technology, which has received great attention. A recent achievement about the MSIF-based filtering and control is summarized, which includes the MSIF filter and MSIF control. The filtering algorithm, the linear and nonlinear fusion control, and economic system control are extensively reviewed. Some typical application results of MSIF are given, and open issues on multi-sensor information fusion filtering and control are also pointed out.
Machine Learning and Cognitive Technology for Intelligent Wireless Networks

The ability to dynamically and efficiently allocate resources to meet the need of growing diversity in services and user behavior marks the future of wireless networks, giving rise to intelligent processing, which aims at enabling the system to perceive and assess the available resources, to autonomously learn to adapt to the perceived wireless environment, and to reconfigure its operating mode to maximize the utility of the available resources. The perception capability and reconfigurability are the essential features of cognitive technology while modern machine learning techniques project effectiveness in system adaptation. In this paper, we discuss the development of the cognitive technology and machine learning techniques and emphasize their roles in improving both spectrum and energy efficiency of the future wireless networks. We describe in detail the state-of-the-art of cognitive technology, covering spectrum sensing and access approaches that may enhance spectrum utilization and curtail energy consumption. We discuss powerful machine learning algorithms that enable spectrum- and energy-efficient communications in dynamic wireless environments. We also present practical applications of these techniques to the existing and future wireless communication systems, such as heterogeneous networks and device-to-device communications, and identify some research opportunities and challenges in cognitive technology and machine learning as applied to future wireless networks.
Studying Co-Tagging Networks in Learning Object Repositories

Co-tagging refers to the process of adding the same tag to one or more digital resources by two or more taggers. This process can be modelled as a social network, also known as co-tagging network, where the actors are the taggers and the relationships between them are the common tags that they have used. Co-tagging networks have been studied by applying social network analysis (SNA) metrics so as to: (a) draw significant insights about the influence of central users to the (tagging) behaviour of other users and (b) identify clusters of taggers based on the content of the tags that they use. Nevertheless, there are no previous works, which have studied co-tagging networks in the field of Learning Object Repositories (LORs). In this paper we aim to address this issue by studying the co-tagging network of a specific LOR, namely the OpenScienceResources (OSR) Repository.
Joint Learning and Optimization of Unknown Combinatorial Utility Functions

This work considers the problem of automatically discovering the solution preferred by a decision maker (DM). Her preferences are formalized as a combinatorial utility function, but they are not fully defined at the beginning and need to be learnt during the search for the satisficing solution. The initial information is limited to a set of catalog features from which the decisional variables of the DM are to be selected. An interactive optimization procedure is introduced, which iteratively learns an approximation of the utility function modeling the quality of candidate solutions and uses it to generate novel candidates for the following refinement. The source of learning signals is the decision maker, who is fine-tuning her preferences based on the learning process triggered by the presentation of tentative solutions. The proposed approach focuses on combinatorial utility functions consisting of a weighted sum of conjunctions of predicates in a certain theory of interest. The learning stage exploits the sparsity-inducing property of 1-norm regularization to learn a combinatorial function from the power set of all possible conjunctions of the predicates up to a certain degree. The optimization stage consists of maximizing the learnt combinatorial utility function to generate novel candidate solutions. The maximization is cast into an Optimization Modulo Theory problem, a recent formalism allowing to efficiently handle both discrete and continuous-valued decisional features. Experiments on realistic problems demonstrate the effectiveness of the method in focusing towards the optimal solution and its ability to recover from suboptimal initial choices.
Triaqua(benzene-1,3-dicarboxylato)(4,5-diazafluoren-9-one)cadmium(II) pentahydrate

In the title compound, [Cd(C8H4O4)(C11H6N2O)(H2O)3]·5H2O, the CdII atom is seven-coordinated by two N atoms from one bidentate phenanthroline-derived ligand and by five O atoms, two from one bidentate benzene-1,3-dicarboxylate (1,3-BDC) ligand and three from water molecules, in a distorted pentagonal-bipyramidal geometry. Neighbouring units interact through π–π interactions [centroid–centroid distances = 3.380 (3) and 3.283 (4) Å]. Finally, three types of O—H⋯O hydrogen bonds exist between coordinated dissociative water molecules and hybridization water molecules and carboxylate O atoms, resulting in a two-dimensional network parallel to (010).
INCENTIVES FOR PUBLIC DISCLOSURE BY CORPORATE INSIDERS †

It is well understood that when corporate insiders trade shares in their firms stock for the sole purpose of maximizing trading profits, they benefit from asymmetric information, and consequently, favor minimal corporate financial disclosure requirements. We demonstrate that when insiders are risk-averse and have other motives for trade, such as liquidity needs, they can actually be harmed by asymmetric information, which increases trading costs. Consequently, insiders could favor full disclosure over nondisclosure, and generally prefer an intermediate level of disclosure, in which they give up some but not all of their information advantage.
Behind the scenes in an interactive speech translation system

This paper describes the facilities of Converser for Healthcare 4.0, a highly interactive speech translation system which enables users to verify and correct speech recognition and machine translation. Corrections are presently useful for real-time reliability, and in the future should prove applicable to offline machine learning. We provide examples of interactive tools in action, emphasizing semantically controlled back-translation and lexical disambiguation, and explain for the first time the techniques employed in the tools’ creation, focusing upon compilation of a database of semantic cues and its connection to third-party MT engines. Planned extensions of our techniques to statistical MT are also discussed.
Reciprocal synapses between mushroom body and dopamine neurons form a positive feedback loop required for learning

Current thought envisions dopamine neurons conveying the reinforcing effect of the unconditioned stimulus during associative learning to the axons of Drosophila mushroom body Kenyon cells for normal olfactory learning. Here, we show using functional GFP reconstitution experiments that Kenyon cells and dopamine neurons from axoaxonic reciprocal synapses. The dopamine neurons receive cholinergic input via nicotinic acetylcholine receptors from the Kenyon cells; knocking down these receptors impairs olfactory learning revealing the importance of these receptors at the synapse. Blocking the synaptic output of Kenyon cells during olfactory conditioning reduces presynaptic calcium transients in dopamine neurons, a finding consistent with reciprocal communication. Moreover, silencing Kenyon cells decreases the normal chronic activity of the dopamine neurons. Our results reveal a new and critical role for positive feedback onto dopamine neurons through reciprocal connections with Kenyon cells for normal olfactory learning. DOI: http://dx.doi. org/10.7554/eLife.23789. 001
Geranylgeranyl-Pyrophosphate, an Isoprenoid of Mevalonate Cascade, Is a Critical Compound for Rat Primary Cultured Cortical Neurons to Protect the Cell Death Induced by 3-Hydroxy-3-Methylglutaryl-CoA Reductase Inhibition

We investigated the role of the intrinsic mevalonate cascade in the neuronal cell death (NCD) induced by the inhibition of 3-hydroxy-3-methylglutaryl-CoA (HMG-CoA) reductase in rat primary cortical neurons cultured from the brains of 17-d-old fetal SD rats. HMG-CoA reductase inhibitors induced NCD [HMG-CoA reductase inhibitor-induced NCD (H-NCD)] in time- and dose-dependent manners. The apoptotic characteristics were revealed by the formation of the DNA ladder and by the electron microscopical observation. During the progression of H-NCD, p53 was induced followed by the expression of Bax. Although the mevalonate completely inhibited H-NCD, the cholesterol did not. Thus, we examined two major metabolites of mevalonate, geranylgeranyl-pyrophosphate (GGPP) and farnesyl-pyrophosphate (FPP), using a novel liposome system for uptake into the cells. GGPP, not FPP, prohibited H-NCD with inhibition of the induction of p53 and Bax. The inhibition of HMG-CoA reductase decreased the amount of membrane-associated Rho small GTPase families, but not Ras small GTPase, and GGPP restored the blockage by HMG-CoA reductase inhibitor in the translocation or redistribution of Rho small GTPase families to membrane. These data indicated that (1) the inhibition of the intrinsic mevalonate cascade induces the apoptotic NCD with the induction of p53 followed by that of Bax, (2) the inhibition of HMG-CoA reductase concomitantly causes blockage of the translocation or redistribution of Rho small GTPase families, not Ras small GTPase, to membrane, and (3) GGPP, not FPP, is one of the essential metabolites in the mevalonate cascade for protecting neurons from H-NCD.