Miracle-dz/UniFAR-FTUs huggingface dataset:
The role of syntax and semantics in machine translation and quality estimation of machine-translated user-generated content

The availability of the Internet has led to a steady increase in the volume of online user-generated content, the majority of which is in English. Machine-translating this content to other languages can help disseminate the information contained in it to a broader audience. However, reliably publishing these translations requires a prior estimate of their quality. This thesis is concerned with the statistical machine translation of Symantec's Norton forum content, focusing in particular on its quality estimation (QE) using syntactic and semantic information. We compare the output of phrase-based and syntax-based English-to-French and English-to-German machine translation (MT) systems automatically and manually, and nd that the syntax-based methods do not necessarily handle grammar-related phenomena in translation better than the phrase-based methods. Although these systems generate suciently dierent outputs, the apparent lack of a systematic dierence between these outputs impedes its utilisation in a combination framework. To investigate the role of syntax and semantics in quality estimation of machine translation, we create SymForum, a data set containing French machine translations of English sentences from Norton forum content, their post-edits and their adequacy and uency scores. We use syntax in quality estimation via tree kernels, hand-crafted features and their combination, and nd it useful both alone and in combination with surface-driven features. Our analyses show that neither the accuracy of the syntactic parses used by these systems nor the parsing quality of the MT output aect QE performance. We also nd that adding more structure to French Treebank parse trees can be useful for syntax-based QE. We use semantic role labelling (SRL) for our semantic-based QE experiments. We experiment with the limited resources that are available for French and nd that a small manually annotated training set is substantially more useful than a much larger articially created set. We use SRL in quality estimation using tree kernels, hand-crafted features and their combination. Additionally, we introduce PAM, a QE metric based on the predicate-argument structure match between source and target. We nd that the SRL quality, especially on the target side, is the major factor negatively aecting the performance of the semantic-based QE. Finally, we annotate English and French Norton forum sentences with their phrase structure syntax using an annotation strategy adapted for user-generated text. We nd that user errors occur in only a small fraction of the data, but their correction does improve parsing performance. These treebanks (Foreebank) prove to be useful as supplementary training data in adapting the parsers to the forum text. The improved parses ultimately increase the performance of the semantic-based QE. However, a reliable semantic-based QE system requires further improvements in the quality of the underlying semantic role labelling.
A Framework for Mining High Utility Web Access Sequences

Abstract Mining web access sequences (WASs) can discover very useful knowledge from web logs with broad applications. By considering non-binary occurrences of web pages as internal utilities in WASs, e.g., time spent by each user in a web page, more realistic information can be extracted. However, the existing utility-based approach has many limitations such as considering only forward references of web access sequences, not applicable for incremental mining, suffers in the level-wise candidate generation-and-test methodology, needs several database scans and does not show how to mine web access sequences with different impacts/significances for different web pages. In this paper, we propose a novel framework to solve these problems. Moreover, we propose two new tree structures, called utility-based WAS tree (UWAS-tree) and incremental UWAS-tree (IUWAS-tree) for mining WASs in static and incremental databases, respectively. Our approach can handle both forward and backward references, static and incremental data, avoids the level-wise candidate generation-and-test methodology, does not scan databases several times, and considers both internal and external utilities of a web page. The IUWAS-tree is also applicable for interactive mining. Extensive performance analyses show that our approach is very efficient for both static and incremental mining of high utility WASs.
Learning Objects and Semantic Web in Education: From Students' Analysis to New Perspectives for Their Use

The work aims at giving a new perspective in ICT use in education by integrating web technologies in traditional teaching-learning processes. First it proposes the analysis of the difﬁculties students meet while attending both traditional and on line courses, then the factors inﬂuencing students’ performances are discussed and alternative assessment instruments based on ICT use are reported. At last recent hypotheses on the introduction of learning objects and semantic Web in education are analyzed and e-learning strategies and instruments are compared with the results from traditional teaching contexts. As a consequence an adaptive e-learning strategy is suggested.
Probing emergent geometry in speech models via replica theory

The success of deep neural networks in visual tasks have motivated recent theoretical and empirical work to understand how these networks operate. Meanwhile, deep neural networks have also achieved impressive performance in audio processing applications, both as sub-components of larger systems or as complete end-to-end systems. In this work, we employ a recently developed statistical mechanical theory that connects geometric properties of network representations with class separability to probe how information is untangled within neural networks trained to recognize speech. We find that speech recognition models carry out significant layerwise and temporal untangling of words by efficiently extracting task-relevant features. This untangling results from a decrease in the per-class radius and dimension, and a reduction in the correlation between class centers.
Credit Data Fraud Detection using Kernel Methods with Support Vector Machine

This paper presents binary Support Vector Machine classifiers for detection of fraudulent credit card transactions. Support Vector Machine (SVM) is a well-known method in statistical learning theory for classification and regression problems. The objective of this paper is about evaluation of accuracy and performance of different kernel methods using Sequential Minimal Optimization (SMO), C-Support Vector Classification (C-SVC) and υ-Support Vector Classification (υ-SVC). A Comparison of computation time and classifiers’ accuracy for SMO classifier with C-SVC in LIBSVM is provided. The simulation results show that accuracy of SMO classifier in with different kernel functions is higher than C-SVC in LIBSVM on credit data dataset obtained from UCI machine learning database.
Fund Investment Decision in Support Vector Classification Based on Information Entropy

As to the complex investment decision with big data, how to portray the essential characteristics to answer its evolving complexity of the mechanism, and how to make risk identification, assessment and measurement have become problems which urgently need to be addressed by investors. In this paper, the support vector classification based on information entropy (IE-SVC) is put forward to improve the accuracy in the field of capital investment decisions. Two classic methods, the K-Nearest Neighbors algorithm (K-NN) and the Radius Basis Function Neural Network (RBFNN), are applied to compare the performance. In the experiment of Gates foundation investment decision, its results show that the IE-SVC can be faster and higher accuracy than those of other methods.
A 10 GHz SiGe BiCMOS phase-locked-loop frequency synthesizer

A SiGe BiCMOS phase-lock-loop circuit is presented. A maximum operational frequency of 10 GHz and a current consumption of 7.6 mA, i.e. 17 mW is demonstrated. For a 9 mW low power version, a maximum frequency of 4.7 GHz is determined. This demonstrates the speed and power advantage of the SiGe BiCMOS technology for wireless communications.
Schildklierfunctie, dagelijks functioneren en overleving van de oudste ouderen; de 'Leiden 85-plus Studie'

Objective. To determine the relationship between differences in thyroid function, changes in the activities of daily living and survival in the extremely-old segment of the general population in order to see whether screening for and treatment of subclinical thyroid-function disorders in the elderly will have a positive effect. Design. Prospective observational population study among 85-year-olds. Method. As part of the 'Leiden 85-plus Study', all persons were followed who had their 85th birthday during the period from I September I997 to 31 August I999 (average length of time followed: 3.7 years; SD: I.4). There were 558 participants. The thyroid function of these subjects was determined and the limitations in the activities of daily living, depressive symptoms, cognitive function and mortality were recorded annually. Results. At the age of 85, there was no relation between the serum levels of thyroid-stimulating hormone (TSH) or free thyroxine (FT 4 ) and limitations in the activities of daily living, the occurrence of depressive symptoms and cognitive deterioration. Neither was any relationship found during the period of follow-up. A higher TSH-level was associated with a lower mortality, even after correction for the differences in performance and health during the base measurement (mortality risk: 0.77 per SD-increase in TSH; 95% CI: 0.63-0.94). The mortality risk per SD-increase in FT, was 1.16 (95% CI: 1.04-1.30). Conclusion. From the age of 85, there was no relationship between thyroid function and limitations in the activities of daily living, the occurrence of depressive symptoms or a deterioration in cognitive functions. Moreover, elderly persons with a less active thyroid gland lived longer. This raises the question whether the screening for and treatment of subclinical thyroid-function disorders in persons of extreme old age, as recommended, will have any positive effects.
Incorporating Chinese Radicals Into Neural Machine Translation: Deeper Than Character Level

In neural machine translation (NMT), researchers face the challenge of un-seen (or out-of-vocabulary OOV) words translation. To solve this, some researchers propose the splitting of western languages such as English and German into sub-words or compounds. In this paper, we try to address this OOV issue and improve the NMT adequacy with a harder language Chinese whose characters are even more sophisticated in composition. We integrate the Chinese radicals into the NMT model with different settings to address the unseen words challenge in Chinese to English translation. On the other hand, this also can be considered as semantic part of the MT system since the Chinese radicals usually carry the essential meaning of the words they are constructed in. Meaningful radicals and new characters can be integrated into the NMT systems with our models. We use an attention-based NMT system as a strong baseline system. The experiments on standard Chinese-to-English NIST translation shared task data 2006 and 2008 show that our designed models outperform the baseline model in a wide range of state-of-the-art evaluation metrics including LEPOR, BEER, and CharacTER, in addition to BLEU and NIST scores, especially on the adequacy-level translation. We also have some interesting findings from the results of our various experiment settings about the performance of words and characters in Chinese NMT, which is different with other languages. For instance, the fully character level NMT may perform well or the state of the art in some other languages as researchers demonstrated recently, however, in the Chinese NMT model, word boundary knowledge is important for the model learning.
A state predictor-based reinforcement learning system

The present paper proposes a new reinforcement learning (RL) system called a state predictor-based RL system in order to solve the explosion of state space and create cooperative behaviors in multiagent systems. The proposed system realizes a predictive function by representing both the present and the next state-action groups with ITPM, an incremental topology map. The proposed system is applied to the pursuit problem, and its performance is evaluated by comparison with the conventional RL method in computer simulations. The experimental results show that the proposed system can appropriately learn in a complex environment which is hardly solved by the conventional RL. Furthermore, it is confirmed that the proposed system can acquire cooperative strategies. © 2010 Wiley Periodicals, Inc. Electron Comm Jpn, 93(6): 8–18, 2010; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ecj.10258
Different techniques used to improve the performance of a classifier of the twelve-lead electrocardiogram

Investigates the automatic classification of the 12-lead electrocardiogram (ECG) into different pathophysiological disease categories. The ECG database used in this study contained 926 recordings, with 500 records classified with 100% accuracy and 426 records classified with 75% accuracy. Each record contained a simultaneously recorded 12-lead ECG of 8-10 s duration. Each record is classed as either (i) normal; (ii) left, right or bi-ventricular hypertrophy; or (iii) anterior, inferior or combined myocardial infarction. A baseline classifier was trained using a single beat from the 500 classified recordings and resulted in a 7-way classification test-set accuracy of 55%. The following techniques were used for improving the classification performance: (1) multi-beat data, (2) regularisation of the covariance matrix, and (3) utilisation of inaccurately classified data in the training process. Combining these three techniques resulted in a classifier with a test-set accuracy of over 75%.
INDECISIVE DATA CLASSIFICATION FOR DISCRIMINATIVE PATTERNS MINING USING SVM

Apposite to the impenetrability in judgment real indecisive data, obtainable works on indecisive data mining simply employ whichever synthetic datasets or real datasets with synthetically produce probability value. This presents these mechanisms a mostly hypothetical flavour, wherever appliance domain is theoretical. In dissimilarity, in this paper the primary challenge to be appropriate indecisive data mining method real world appliance such as noise classification and clustering. Moreover, beyond the creation of indecisive features, this methodology is domain independent and consequently could be effortlessly extensive and estimate in other domains. In this research, we exploit the regularize framework and proposed an associative classification algorithm for uncertain data. The major recompense of SVM (support vector machine) is: recurrent itemsets capture every the dominant associations between items in a dataset. These classifiers naturally handle missing values and outliers as they only deal with statistically significant associations which build the classification to be vigorous. Extensive performance analysis has exposed such classifiers to be recurrently more precise. We proposed a novel indecisive SVM Based clustering algorithm which considers large databases as the major application. The SVM Based clustering algorithm will cluster a specified set of data and exploit the matching other works
A linear recursive bad data identification method with real-time application to power system state estimation

The recursive measurement error estimation identification (RMEEI) method for bad data (BD) analysis is described. By using a set of linear recursive formulae, state variables, residuals, and their variances are updated after the removal of a measurement from suspected data set to the remaining data set (or in the reverse direction). Neither a reestimation nor residual sensitivity matrix is needed in the identification process, which increases the computational speed greatly. Digital tests have been done to compare the RMEEI method with other conventional BD identification method in identification performance and computational speed. The real-time operation experience of the RMEEI method in energy management system (EMS) of the North East China power system control center is given. <<ETX>>
A Nonlinear Programming Technique for the Optimization of Continuous Processing Systems

A description is given of a method for solving some nonlinear programming problems. The mathematics of this method are quite simple and are easy to apply to electronic computation. A numerical example, a model construction example, and a description of a particular existing computer system are included in order to clarify the mode of operation of the method.
An Adaptive, Personalised Chording Keyboard

We present a design for personalisation of a chording keyboard. There are two primary design goals. Firstly, the keyboard layout should be easy to learn, and easy to use, taking into account the background and physical constraints of the user. Secondly, the keyboard layout should be readily extensible, based on the previous behaviour of the keyboard user. The design proposal accomplishes these goals, and can be simply implemented on cost-effective hardware. In addition, we present preliminary experimental results on optimising the initial keyboard layout.
Off-Line Programming Techniques for Multirobot Cooperation System

An off-line programming software designed for multiple robots cooperation system is presented in this paper. At the beginning of the paper, current technologies of robot off-line programming are reviewed and summarized. Then, present status of cooperative functions and cooperative motion instructions defined by predominant robot manufacturers are introduced. Thereafter, principles of robot path planning for cooperative motions are presented. Based on such analysis, software architecture of the off-line programming software designed for general multirobot cooperation system is presented. At last, an off-line programming software with high detailed 3 dimension visualization is achieved. Simulation graphs and snapshots of typical cooperative motions are presented at the end of the paper, which proved the effectiveness of our proposed method.
How many words can my robot learn?: An approach and experiments with one-class learning

This paper addresses word learning for human–robot interaction. The focus is on making a robotic agent aware of its surroundings, by having it learn the names of the objects it can find. The human user, acting as instructor, can help the robotic agent ground the words used to refer to those objects. A lifelong learning system, based on one-class learning, was developed (OCLL). This system is incremental and evolves with the presentation of any new word, which acts as a class to the robot, relying on instructor feedback. A novel experimental evaluation methodology, that takes into account the open-ended nature of word learning, is proposed and applied. This methodology is based on the realization that a robot’s vocabulary will be limited by its discriminatory capacity which, in turn, depends on its sensors and perceptual capabilities. The results indicate that the robot’s representations are capable of incrementally evolving by correcting class descriptions, based on instructor feedback to classification results. In successive experiments, it was possible for the robot to learn between 6 and 12 names of real-world office objects. Although these results are comparable to those obtained by other authors, there is a need to scale-up. The limitations of the method are discussed and potential directions for improvement are pointed out.
The joint impact of mobility models, percentage of transmitting/receiving nodes, and nodes density in the scenario on MANET routing protocols

Abstract:A Mobile Ad-hoc network (MANET) is a multi-hop wireless network where all nodes cooperatively maintain network connectivity without a centralized infrastructure, and nodes change their positions dynamically. Most studies in MANETs are focused on scenarios with special characteristics. These are: Random waypoint mobility model, 40% transmitting/receiving nodes, and high density of mobile nodes in the scenario area. Our framework aims to evaluate the impact of mobility models, network density, and different percentages of transmitting/receiving nodes on the performance of MANET routing protocols. In this paper a study 27 different scenarios which aim to study the joint effect of different densities of mobile nodes in the scenario area; Three different mobility models; as well as different percentages of transmitting/receiving nodes. Each scenario is evaluated for both the location aided routing (LAR) and ad-hoc on demand distance vector (AODV) routing protocols. Performance evaluation metrics included packet delivery ratio, overhead, and the average end-to-end delay. These metrics were evaluated for a speed of the mobile nodes in the range from 1 to 40 meters/sec. We analyzed the results and compiled a catalogue for MANETS that can be used to deploy the best suitable routing protocol for each scenario of choice.
SPREADING WORD: A BEHAVIORAL THEORY OF ONLINE MESSAGE RETRANSMISSION IN A CRISIS

With the emergence of online networks such as Twitter, Weibo, and Facebook, message retransmission has now exploded. People can spread word (retweeting, sharing, and forwarding) faster and more broadly than ever. Depending on the messages retransmitted, such behavior could prove helpful or destabilizing. As a result, there is great interest among crisis managers in understanding message retransmission behavior. However, message retransmission has not received much attention in the literature, and the theories that have been proposed for it are quite limited, especially in the context of crises. In this paper, we focus on message retransmission: the act of relaying a received message on to others, and propose a theory to explain how and why word spreads in a crisis. To answer the how question, we propose a process theoretical model that clarifies key steps of message retransmission in a crisis. To answer the why question, we propose a variance theoretical model that explains how message cues affect recipients’ percep tions which, in turn, drive message retransmission behavior. Our work offers opportunities and new insights for research on online crisis communication and design science. Practically, our theory can inform online communication systems design and offer useful insights for stakeholders involved in a crisis management.