Miracle-dz/UniFAR-FTUs huggingface dataset:
Who’s Next? From Sentence Completion to Conceptually Guided Message Composition

Natural language generation is typically based on messages and goals. We present here our views on how to help people to provide this kind of input, i.e. how to communicate thoughts to the computer, so that it could produce the corresponding surface-forms (sentences). The resource we are building is composed of a linguistically motivated ontology, a dictionary and a graph generator, whose respective functions are (a) guiding the user to make his choices concerning the concepts/words to build the message from, (b) to provide knowledge of how to link the chosen elements to yield a message (compositional rules), and (c) the visual display of the output, i.e. message graph representing the user’s input. Our starting point is Illico, a system developed for French. Yet, being designed for sentence completion rather than message construction, it tends to drown the user by providing too many options, a shortcoming that we try to overcome via the mentionned ontology combined with a tool checking conceptual well formedness. In order to make our goal feasable (allow users to express freely ideas of any sort), we will start by limiting ourselves to two small domains: soccer and textbooks designed for learning French.
Ab initio simulations of Cu binding sites in the N-terminal region of PrP

The prion protein (PrP) binds Cu2+ ions in the octarepeat domain of the N-terminal tail up to full occupancy at pH=7.4. Recent experiments show that the HGGG octarepeat subdomain is responsible for holding the metal bound in a square planar coordination. By using first principle ab initio molecular dynamics simulations of the Car-Parrinello type, the Cu coordination mode to the binding sites of the PrP octarepeat region is investigated. Simulations are carried out for a number of structured binding sites. Results for the complexes Cu(HGGGW)+(wat), Cu(HGGG) and the 2[Cu(HGGG)] dimer are presented. While the presence of a Trp residue and a H2O molecule does not seem to affect the nature of the Cu coordination, high stability of the bond between Cu and the amide Nitrogens of deprotonated Gly's is confirmed in the case of the Cu(HGGG) system. For the more interesting 2[Cu(HGGG)] dimer a dynamically entangled arrangement of the two monomers, with intertwined N-Cu bonds, emerges. This observation is consistent with the highly packed structure seen in experiments at full Cu occupancy.
Multi-class Support Vector Domain Classification and Its Application in Intrusion Detection

There are many traditional multi-class support vector machine methods with low precision used in the network security field.It presents a new intrusion detection method named MSVDC(multi-class support vector domain classification) after analyzing and studying with the network connection data.This method extends SVDC(support vector domain classification) to solve the multi-class problem.Experimental results carried out on KDD CUP1999 data set show that the proposed method performs better both in true positive rate and precision accuracy compared with traditional multi-class support vector machine based intrusion detection methods.
Cross-lingual part-of-speech tagging using word embedding

As one of semi-supervised learning approach, cross-lingual projection leverages existing resources from a resource-rich language when building tools for resource-poor languages. In this paper we attempt to make use of word embedding with anchor based label propagation to improve the accuracy of a cross-lingual projection task: cross-lingual part-of-speech tagging under the graph-based framework. Our approach uses bilingual parallel corpora and labeled data from the resource-rich side assuming that there is no labeled data or only a few labeled data in resource-poor language. The results suggest the efficacy of our approach over traditional label propagation with lexical feature for projecting part-of-speech information across languages, and show that a few of labeled data help to enhance the effect a lot in cross-lingual task.
Improved Background Subtracted Method Based ViBe

ViBe background subtracted algorithm deals with pixels based on RGB color space. So when the light suddenly changed, a large of background area will be misclassified as moving foreground. In addition, ViBe algorithm considered a lot of the moving background pixels to be foreground in the scene. To solve these problem, this paper proposed algorithm which combined with(r,g,I) normalized color space. The experimental results show that the improved algorithm can reaches the more robust extraction for foreground at the abrupt changes of light. At the same time, the improved algorithm has a better effect on detecting the moving background pixels in the scene.
Advanced Techniques for Mapping and Cleaning

We address the challenging and open problem of bringing together two crucial activities in data integration and data quality, i.e., transforming data using schema mappings, and fixing conflicts and inconsistencies using data repairing. This problem is made complex by several factors. First, schema mappings and data repairing have traditionally been considered as separate activities, and research has progressed in a largely independent way in the two fields. Second, the elegant formalizations and the algorithms that have been proposed for both tasks have had mixed fortune in scaling to large databases. In the thesis, we introduce a very general notion of a mapping and cleaning scenario that incorporates a wide variety of features, like, for example, user interventions. We develop a new semantics for these scenarios that represents a conservative extension of previous semantics for schema mappings and data repairing. Based on the semantics, we introduce a chase-based algorithm to compute solutions. Appropriate care is devoted to developing a scalable implementation of the chase algorithm. To the best of our knowledge, this is the first general and scalable proposal in this direction.
Corresponding state behaviour of capillary condensation of confined alkanes

ABSTRACT The corresponding state behaviour of capillary condensation of confined alkanes is studied by means of GC-TMMC simulations. In this study, alkane is considered under graphite and mica slit pore confinements of various slit widths. Corresponding state phase coexistence envelopes of capillary condensation display insignificant effect of surface chemistry for slit pore confinements of 6 Å for methane and 15 Å for n-butane and n-octane. On the other hand, the significant effect of surface chemistry on the corresponding state coexistence envelope is observed for larger slit pore width. Moreover, at a given slit width, more shrinking in the corresponding state coexistence envelope of alkane is observed with the mica slit pore as compared to graphite slit pore. The local behaviour of capillary condensation of corresponding state is also investigated in some typical cases using local density profiles, to understand the overall behaviour of corresponding state capillary condensation. Moreover, for a given slit pore width smaller size alkane has shown more shrinking of the corresponding state coexistence envelope, irrespective of surface chemistry. However, the effect of surface chemistry on the corresponding state coexistence envelope of different alkanes becomes insignificant in smaller slit pore width. Fluctuating positive and negative deviations in the corresponding state vapour–liquid equilibrium pressure with respect to corresponding bulk values are observed in studied slit pore confinements. The corresponding state vapour−liquid interfacial tension of alkanes is less in mica slit pore as compared to graphite slit pore for the studied range of reduced temperatures.
Radical cyclizations as key step for the stereoselective synthesis of Bi- and tricyclic sesquiterpene lactones

A strategy towards the stereoselective synthesis of bi-and tricyclic sesquiterpene lactones is reported. As key step radical cyclizations of appropriately functionalized trans-4,5-disubstituted γ-butyrolactones, which arereadily available from methyl 2-furoate, were carried out to give rise to 5,6-, 5,7- and 5,7,5-ring systems in diastereo- and enantiomerically pure form.
Self-taught learning

We introduce a new machine learning framework called self-taught learning for using unlabeled data in supervised classification tasks. This framework does not require that the unlabeled data follow the class labels of the supervised task, or arise from the same generative distribution. Such unlabeled data is often significantly easier to obtain than in previously studied frameworks such as semi-supervised learning. In this thesis, we demonstrate that self-taught learning can be applied successfully to a variety of hard machine learning problems. The centerpiece of our work is a self-taught learning algorithm based on an optimization problem called "sparse coding. " This algorithm uses unlabeled data to learn a new representation for complex, high-dimensional inputs, and then applies supervised learning over this representation. The representation captures higher-level aspects of the input, and significantly improves classification performance on many test domains, including computer vision, audio recognition and text classification. We present efficient sparse coding algorithms for a translation-invariant version of the model, that can be applied to audio and image data. We also generalize the model to a much broader class of inputs, including domains that are hard to handle with previous algorithms, and apply the model to text classification and a robotic perception task. Taken together, these experiments demonstrate that using the self-taught learning framework, machine learning can be applied to much harder problems than previously possible. These self-taught learning algorithms work best when they are allowed to learn rich models (with millions of parameters) using large amounts of unlabeled data (millions of examples). Unfortunately, with current methods, it can take weeks to learn such rich models. Further, these methods require fast, sequential updates, and with current algorithms, are not conducive to being parallelized on a distributed cluster. To apply self-taught learning to such large-scale problems, we show that graphics processor hardware (available in most modern desktops) can be used to massively parallelize the algorithms. Using a new inherently parallel algorithm, the sparse coding algorithm can be easily implemented on graphics processors, and we show that this can reduce the learning time from about three weeks to a single day. Finally, we consider self-taught learning methods that learn hierarchical representations using unlabeled data. We develop general principles for unsupervised learning of such hierarchical models using graphics processors, and show that the slow learning algorithms for the popular deep belief network model can be successfully parallelized. This implementation is up to 70 times faster than an optimized CPU implementation, reduces the learning time from weeks to hours, and represents the state-of-the-art in learning large deep belief networks.
Compilers Principles Techniques And Tools

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Study on Topic Partition in Automatic Abstracting System

With the development of network,electronic text grows rapidly.Since automatic abstraction is superior to manual abstraction for its speed,convenience,efficiency,and impersonality.It has wide applications and such research is becoming a hot topic.Topic partition is a significant problem during text structuring in automatic abstracting system.The paper establishes vector space model for the whole article based on paragraph,then proposes an algorithm for multi-topic text partitioning based on sequential paragraphic similarity.It solves the problem of chapter structural analysis in multi-topic article and makes the abstract of the multi-topic to have more general content and more balanced structure. The experiment on close test shows that the precision of topic partition for multi-topic text and single-topic text reach 92.2% and 99.1% respectively.
Developing Assessment Rubric in Graphic Design Studio-Based learning: Experiments in Active Teaching in a Case Study

Assessment is an integral part of the teaching and learning process as it determines whether or not the goals of education are being met. Assessment support learning and it is usually the forefront of student’s perception of teaching and learning process. It’s a fact that most creative art and design disciplines faced challenges in assessment. Many studies showed the lack of using some assessments techniques for art and design by teachers in higher education. Research like contest the measuring of creative process, discuss subjectivity and non-transparency, challenge unplanned assessment of creative outputs and the inconsistency in evaluating creative outcomes. So training and guidance on the use of rubrics will help reduce the discrepancies, and intrinsically motivate students to use them for learning. The study aim was to develop and involve rubric in graphic design assessment for case study in studio based learning course. To determine how instructors assisted student’s response to variety of different types of active learning in case study for the Graphic design & illustration course. The Case Study was undertaken by second-year Graphic Design students at the Emirates College of technology in UAE. In this follow-up methodology, the researchers applied adult’s andragogy learning and teaching methods in project-based learning real case study. The students were introduced for the first time to the use of assessment rubrics in assessing their creative performance. This paper defines Performance assessment in active learning methods, present the role of rubric assessment, and offers suggestions for implementing it in assisting a case study in graphic design course, to develop and incorporate assessment procedures that allow creative ability to be assessed with greater transparency and objectivity.
Consistent authentication in disitrbuted networks

In a time where peer-to-peer networks, often with previously unconnected devices, are increasing in relevance, new storage solutions are needed. Storage can no longer rely on a single central entity but rather needs to depend on the resources of the entire network. Such a solution is the distributed hash table (DHT) which allows distributed storage of resources, ensuring redundancy and availability of resources. Common DHT implementations have however been found to be susceptible to several attacks and therefore not suitable for security-critical data. To enable a wider use of DHT, a strengthening which can be easily implemented in existing DHT implementations have to be found. In this thesis, the security of the Kademlia DHT, present in the Ericsson developed framework Calvin, was tested by performing a series of well-known attacks against an existing implementation. From the vulnerabilities found in these tests, security enhancements based on authentication were designed. All new functionality was designed to interfere with the original implementation as little as possible. The Kademlia DHT was strengthened with provable identities, cryptographically signed messages and a certificate distribution scheme. All of this was built on a public key infrastructure having an out-of-band certificate authority. The security enhancements were shown to both hamper known attacks and prevent outsiders from retrieving any information from the DHT. However, overhead and more complex computation were introduced into the system with the security enhancements. Further research is needed to determine if very computationally limited devices can participate or if additional functionality is needed to facilitate this. (Less)
Design of stacking machine for finished food boxes based on motion controller

A kind of finished food boxes stacking machine was designed based on " PC + motion controller". Taking PC as the upper computer and multi axis motion controller as the lower computer,communication between the upper and lower machine used standard Ethernet,HMI and multi axis motion controller communicated via the RS232/485 standard interface,and completed the stacking work via the upper and lower computer.It can realize the function of stacking,reduce the amount of labor,and improve work efficiency.And it provides a new idea for the design of the stacking machine.
Animal Models Of Diabetes A Primer

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Quantum state engineering via coherent-state superpositions in traveling optical fields

We propose two experimental schemes for producing coherent-state superpositions which approximate different nonclassical states conditionally in traveling optical fields. Although these setups are constructed of a small number of linear optical elements and homodyne measurements, they can be used to generate various photon number superpositions in which the number of constituent states can be higher than the number of measurements in the schemes. We determine numerically the parameters to achieve maximal fidelity of the preparation for a large variety of nonclassical states, such as amplitude squeezed states, binomial states, squeezed cat states, and various photon number superpositions. The proposed setups can generate these states with high fidelities and with success probabilities that can be sufficient for practical applications.
Intramolecular ring opening of a 2,3-epoxy alcohol by a xanthate anionic center; stereospecific preparation of 2-mercapto-1,3-diol units

Stereospecific ring openings of optically active 2,3-epoxy alcohols were performed by the reaction of 1, 3, 5, and 7 with carbon disulfide and sodium hydride to give the five-membered xanthates 2, 4, 6, and 8. Both enantiomers of 2-mercapto-1,3-diol triacetates, 11 and 14, were derived from 4 and 6, respectively. The ring opening reaction proceeded at −78°C to −30°C, and the yields were around 80%. However, at a higher temperature between 0°C to room temperature, a complicated reaction took place and led to the formation of two isomers of the cyclic thiol carbonates 15 and 16 from 1 or 5. These processes were also stereospecific, and mechanisms have been proposed. In the case of the 3,4-epoxy alcohol 20, the epoxide ring opening gave the six-membered xanthate 21 stereospecifically.
The Ko-Lee Key Exchange Protocol with Generalized Dihedral Groups

Given an arbitrary abelian group A , one may form the generalized dihedral group D ( A ). As D ( A ) is usually non-abelian, this makes it a possible candidate for use with certain non-commutative key exchange protocols. Speciﬁcally, we examine the security of using D ( A ) with the Ko-Lee key exchange protocol. An appropriate presentation for D ( A ) is developed alongside methods for computing within the group in the context of the Ko-Lee protocol. Lastly we show that for such groups Ko-Lee is susceptible to a polynomial time attack.
Training Data Expansion and Boosting of Convolutional Neural Networks for Reducing the MNIST Dataset Error Rate

Background. Due to that the preceding approaches for improving the MNIST image dataset error rate do not have a clear structure which could let repeat it in a strengthened manner, the formalization of the performance improvement is considered. Objective. The goal is to strictly formalize a strategy of reducing the MNIST dataset error rate. Methods. An algorithm for achieving the better performance by expanding the training data and boosting with ensembles is suggested. The algorithm uses the designed concept of the training data expansion. Coordination of the concept and the algorithm defines a strategy of the error rate reduction. Results. In relative comparison, the single convolutional neural network performance on the MNIST dataset has been bettered almost by 30 %. With boosting, the performance is 0.21 % error rate meaning that only 21 handwritten digits from 10,000 are not recognized. Conclusions. The training data expansion is crucial for reducing the MNIST dataset error rate. The boosting is ineffective without it. Application of the stated approach has an impressive impact for reducing the MNIST dataset error rate, using only 5 or 6 convolutional neural networks against those 35 ones in the benchmark work.
A fully discriminative training framework for Statistical Machine Translation (Un cadre d’apprentissage intégralement discriminant pour la traduction statistique) [in French]

A fully discriminative training framework for Statistical Machine Translation A major pitfall of existing statistical machine translation systems is their lack of a proper training procedure. In fact, the phrase extraction and scoring processes that underlie the construction of the translation model typically rely on a chain of crude heuristics, a situation deemed problematic by many. In this paper, we recast machine translation in the familiar terms of a probabilistic structure learning problem, using a standard log-linear parameterization. The tractability of this enterprise is achieved through an efficient implementation that can take into account all the aspects of the underlying translation process through latent variables. We also address the reference reachability issue by using oracle decoding techniques. This approach is experimentally contrasted with a state-of-the-art system on the French-English BTEC translation task.