Miracle-dz/UniFAR-FTUs huggingface dataset:
A hybrid disambiguation model for prepositional phrase attachment

Prepositional Phrase (PP) attachment is a major cause of structural ambiguity in natural language. Many proposals have increasingly relied on large-scale corpus to resolve this problem. However, this approach encounters the notorious sparse-data problem that produces poor results on disambiguation. We in this paper offer a hybrid method which integrates corpus-based approach with knowledge-based techniques for PP attachment disambiguation. It explores a wide-variety of information, including co-occurrence frequencies from annotated corpora, conceptual relationships and conceptual features from a machine-readable dictionary, and syntactic clues from our linguistic observations. We use dictionary definitions and human knowledge to overcome the sparse-data problem. An experiment shows an accuracy rate of 87.7% of our method over 3043 sentences in real English text that contain ambiguous PPs. This result is better than those of any existing methods
Expressive re-performance

Many music enthusiasts abandon music studies because they are frustrated by the amount of time and effort it takes to learn to play interesting songs. There are two major components to performance: the technical requirement of correctly playing the notes, and the emotional content conveyed through expressivity. While technical details like pitch and note order are largely set, expression, which is accomplished through timing, dynamics, vibrato, and timbre, is more personal. This thesis develops expressive re-performance, which entails the simplification of technical requirements of music-making to allow a user to experience music beyond his technical level, with particular focus on expression. Expressive re-performance aims to capture the fantasy and sound of a favorite recording by using audio extraction to split the original target solo and giving expressive control over that solo to a user. The re-performance experience starts with an electronic mimic of a traditional instrument with which the user steps-through a recording. Data generated from the users actions is parsed to determine note changes and expressive intent. Pitch is innate to the recording, allowing the user to concentrate on expressive gesture. Two pre-processing systems, analysis to discover note starts and extraction, are necessary. Extraction of the solo is done through user provided mimicry of the target combined with Probabalistic Latent Component Analysis with Dirichlet Hyperparameters. Audio elongation to match the users performance is performed using time-stretch. Instrument interfaces used were Akais Electronic Wind Controller (EWI), Fender's Squier Stratocaster Guitar and Controller, and a Wii-mote. Tests of the system and concept were performed using the EWI and Wii-mote for re-performance of two songs. User response indicated that while the interaction was fun, it did not succeed at enabling significant expression. Users expressed difficulty learning to use the EWI during the short test window and had insufficient interest in the offered songs. Both problems should be possible to overcome with further test time and system development. Users expressed interest in the concept of a real instrument mimic and found the audio extractions to be sufficient. Follow-on work to address issues discovered during the testing phase is needed to further validate the concept and explore means of developing expressive re-performance as a learning tool.
Desenvolvimento de um gerador de "Scheduling" para uma industria de produção sob encomenda : uma abordagem baseada no uso de controladores difusos e algoritmos geneticos

Traditionally managers concerning on design individual production shops to improve the process. But, manufactures are becoming aware that the major gains in competitiveness made necessary by the growing challenge of global markets, are available only from integrated production planning. This work aims to answer this question by developing a new model to solve this new challenge. A shop floor control system consisting of two hierarchical control levels (shop and machine control) is proposed. It aims to produce on-line scheduling to execute the activities necessary to process an order. A system based on Simulation, Genetic Algorithms and Fuzzy Controllers to find the best schedule order is proposed. The dynamical approach is necessary because usually the real situation cannot be planned, i.e., some events cannot be treated before they arise. These events have notable influence on the systems performance, and they cannot be disregarded. The Genetic Algorithms, a powerful optimization algorithm, are search algorithms based on the mechanics o f natural selection and natural genetics. They exploit historical information to speculate on new search points with expected improved performance. It is applied in this system to tune the fuzzy parameters of fuzzy controllers, that are used to control the despatch of parts. The system was tested in a real industry that produces various kind of product in small volumes. This plant can produce around 1000 parts of 100 products. The throughput is around 200 products a year. It is important to point out that in this kind of job shop type production, in which each job has a different work-flow, efficient management is extremely required. The single rules are compared with this dynamical choosing process. The proposed system is verified by scheduling simulation to be effective in decreasing maximum lateness to due-date as much as average lateness.
Online Simultaneous Learning Projection Matrix and Sparsifying Dictionary

An online algorithm is proposed in this letter to optimize the Projection Matrix and Sparsifying Dictionary (PMSD) simultaneously on a large training dataset. A closedform solution is derived for optimizing the projection matrix with a fixed sparsifying dictionary and the stochastic method is used to optimize the sparsifying dictionary with a fixed optimized projection matrix on a large training dataset to form the proposed online algorithm. Benefiting from training on a large dataset, the proposed algorithm yields a much better performance in terms of signal recovery accuracy than the existing ones. The simulation results on natural images demonstrate its effectiveness and efficiency compared with the existing methods.
Live Streaming Teaching Applied in Real English Classroom

Distance education technology has been broadly applied in various subjects. It also leads to more revolution to teaching models, such as MOOC, flipped classroom, and live streaming teaching. To evaluate the efficacy of live streaming teaching compared to real class teaching, we investigate its application in two phrases—voice streaming and camera streaming. The study implements these two phrases facilitated by an e-community group to achieve class management. Under the guideline of positivism, this study developed a survey to examine live streaming teaching in respect of its conduct, learner acceptance, and teaching efficiency. It was found that it could assist learning in timing and location flexibility, interaction quality, and material variety, offering insights on open learning. Based on these, we conclude that there is fundamental need for further exploration with this new teaching method to advance the science of learning.
Synthetic Over Sampling Methods for Handling Class Imbalanced Problems : A Review

Class imbalanced commonly found in any real cases. Class imbalanced occur if one of the classes has smaller amount, called minority class, than other class (majority class). The problem of imbalanced data is usually associated with misclassification problem where the minority class tends to be misclassified as compared to the majority class. There are two approaches should be performed to solve imbalanced data problems, those are solution at data level and solution at algorithm level. Over sampling approach is used more frequently than the other data level solution methods. This study gives review of synthethic over sampling methods for handling imbalance data problem. The implementation of different methods will produce different characteristics of the generated synthetic data and the implementation of appropriate methods must be adapted to the problems faced such as the level and pattern of imbalanced data of data available. Results of the review show that there is no absolute methods that are more efficient in dealing with the class imbalance. However, the class imbalance problem depends on complexity of the data, level of class imbalance, size of data and classifier involved. Determination of over sampling strategy will affect the outcome of the over sampling. So it is still open better development oversampling methods for handling the class imbalance. The selection classifier and evaluation measures are important to get the best results. Statistical test approach is needed to assess the theoritical propertis of synthetic data and evaluate missclassification in addition to the evaluation methods that have been used.
Formal verification of a static analyzer: abstract interpretation in type theory

This invited talk describes the logical foundations and the status of the ongoing Verasco project, whose aim is to formalize and prove sound a static analyzer for the C programming language based on abstract interpretation, using the Coq proof assistant. (Joint work with David Pichardie, Sandrine Blazy, Jacques-Henri Jourdan, and Vincent Laporte.)
KMS re-contextualization – recognizing learnings from OMIS research

Purpose Deployment of knowledge management systems (KMSs) suffers from low adoption in organizational reality that is attributed to a lack of perceivable added value for people in actual work situations. Poor task/technology fit in the process of knowledge retrieval appears to be a major factor influencing this issue. Existing research indicates a lack of re-contextualizing stored information provided by KMSs in a particular situation. Existing research in the area of organizational memory information systems (OMISs) has thoroughly examined and widely discussed the topic of re-contextualization. The purpose of this paper, thus, is to examine how KMS design can benefit from OMIS research on approaches for re-contextualization in knowledge retrieval. Design/methodology/approach This paper examines OMIS literature and inductively derives a categorization scheme for KMS according to their strategy of re-contextualizing knowledge. The authors have validated the scheme validated in a multiple case study that examines the differentiatory value of the scheme for approaches with various re-contextualization strategies. Findings The classification scheme allows a step-by-step selection of approaches for re-contextualization of information in KMS design and development derived from OMIS research. The case study has demonstrated the applicability of the developed scheme and shows that the differentiation criteria can be applied unambiguously. Research limitations/implications Because of the chosen case study approach for validation, the validation results may lack generalizability. Practical implications The scheme enables an informed selection of KMSs appropriate for a particular OMIS use case, as the scheme’s attributes serve as design rationale for a certain architecture or constellation of components. Developers can not only select from various approaches when designing re-contextualizaton but also come up with rationales for each candidate because of structured representation. Hence, stakeholders can be supported in a more informed way and design KMSs more effectively along organizational change processes. Originality/value The paper addresses an identified need for systematic characterization of KMS approaches and systems intending to meet the objectives of OMISs. As such, it allows streamlining further research in this field, as approaches can be judged according to their originality and positioned relative to each other.
Speech recognition with very large size dictionary

This paper proposes a new strategy, the Multi-Level Decoding (MLD), that allows to use a Very Large Size Dictionary (VLSD, size more than 100,000 words) in speech recognition. MLD proceeds in three steps:\bulleta Syllable Match procedure uses an acoustic model to build a list of the most probable syllables that match the acoustic signal from a given time frame. \bulletfrom this list, a Word Match procedure uses the dictionary to build partial word hypothesis. \bulletthen a Sentence Match procedure uses a probabilistic language model to build partial sentence hypothesis until total sentences are found. An original matching algorithm is proposed for the Syllable Match procedure. This strategy is experimented on a dictation task of French texts. Two different dictionaries are tested,\bulletone composed of the 10,000 most frequent words,\bulletthe other composed of 200,000 words. The recognition results are given and compared. The error rate on words with 10,000 words is 17.3%. If the errors due to the lack of coverage are not counted, the error rate with 10,000 words is reduced to 10.6%. The error rate with 200,000 words is 12.7%.
Beampattern for Multiple Antennas in Hybrid Terrestrial Satellite Communications System (HTSCS)

The hybrid architecture of Terrestrial and Satellite networks discussed in this paper utilizes frequency reuse. However, at the same time the frequency reuse results in Co-Channel Interference (CCI). The CCI is caused by the mobile users to the satellite end because of the strong receiver on the satellite end. Mainly, this paper will focus on to tone down the CCI and would also show that how the OFDM based adaptive beamforming can be employed to mitigate this interference. The technique which is being used to mitigate this interference is Pre-FFT adaptive beamforming also called as time domain beamforming. In this paper, main task is to mitigate the CCI which is induced by the mobile users to the satellite end and will be considered that there are J users. Out of these J users there is one desired user and rest are interferers. When the interfered data is received at the satellite end, the Pre-FFT adaptive beamforming extracts the desired user data from the interferers by applying the complex weights to the received symbol. The weight for the next symbol is then updated by Least Mean Square (LMS) algorithm and then is applied to it. This process is carried out till all the desired user data is extracted from the interference signal.
Employee assistance programs (EAPs): tools for quality of work life

With today’s global challenges and workplace demands, organizations are compelled to institute effective intervention programs to improve the quality of work life of employees. This article aims to assess staff perceptions on the quality of work life of employees via Employee Assistance Programs (EAPs). The study was conducted with a sample of 85 employees in a public sector organization. Data were collected using a pre-coded questionnaire that was self-developed. The analysis of data was conducted utilizing descriptive and inferential statistics, and hypotheses testing formed part of the study. The psychometric properties were assessed using Factor Analysis and Cronbach’s Coefficient Alpha. The study unfolded the impact that EAPs have on the quality of work life of employees. The article culminates with a discussion of recommendations and conclusions.
Scaling kernel machine learning algorithm via the use of GPUs

• Assign each thread to evaluates the sum corresponding to one yj • Steps: 1. Load yj corresponding to the current thread in to a local register. 2. Load the first block of xi to the shared memory. 3. Evaluate part of kernel sum corresponding to xi’s in the shared memory. 4. Store the temporary result in a local register. 5. If all the xi’s have not been processed yet, load the next block of xi’s, go to Step 3. 6. Write the sum in the local register to the global memory. Kernel Density Estimation Ranking
ASSESSING TEACHERS PERCEPTION ON THE EFFICIENCY (SUCCESS) OF INDUCTIVE APPROACH IN AN ESL/EFL CLASSROOM: GRAMMAR IN CONTEXT

The present research attempts to assess teachers' perception on the success of using inductive approach in the classroom at Preparatory Year Program, Najran University. It also inquires the difficulties that are usually faced by students (in teachers' opinion) in a grammar class. In order to collect data, 20 teachers were requested to fill in a questionnaire consisting of ten statements (based on key elements of inductive teaching). The questionnaires were analyzed by using 5-Point Likert-scales of agreement. Besides, the researcher also personally interviewed the teachers by using a set of certain questions covering the same theme. The study is divided into two parts; the first part contains detailed analysis and discussion on the statements of the questionnaire and the second part comprises a detailed analysis and discussion on the responses of interview. As results, it is revealed that a majority of teachers supported the use of inductive approach in the classroom because of its learner-centred nature. Inductive methods help students acquire the critical thinking and self-directed learning skills (Prince & Felder, 2007). However, some teachers (with a negligible percentage) were not so enthusiastic about using inductive approach.
Boltay Hath for Indian Sign Language Recognition

In this research work it is discussed that how a gesture recognition system for alphabetical hand gestures is built. The main motive was to develop a system that can simplify the way that the normal human being interact with the deaf and dump people without the need of interpretator . This system also provides the easy way of humans interaction with computers. This system is designed using MATLAB instead of neural network. In this system Eigen vector and Eigen value techniques only in MATLAB are used. The basis of our method is a fast detection process to obtain the meaningful hand region from the whole image, which is able to deal with a large number of hand gestures against different indoor backgrounds and lighting condition, and a recognition process that identifies the hand gestures from the images of the normalized hand. Experiments show that the system can achieve a 99% recognition rate and is suitable for real-time applications.
Current Trends in Translation Teaching and Learning in 2014

For the past two decades interpreter trainers have been wondering not whether retour interpreting should be taught, but how it can be taught (Harris, 1990, 1992; Snelling, 1992). The European Higher Education Area (EHEA) offers new opportunities for the proliferation of networks and exchanges of didactic materials or methodologies for the teaching of interpreting into B. First, we critically appraise the traditional dichotomy between models based on constructivism and liberalism in interpreter training. We then present those competences inherent to retour interpreting and describe the joint edition of a DVD featuring didactic materials and speeches in its initial phases. We finally discuss our experience in the use and evaluation of the teaching materials for the learning and teaching of simultaneous interpreting into B from Spanish into English and into German with the aim of fostering those skills.
Investigations of Fully Homomorphic Encryption (IFHE)

Abstract : The IFHE project Investigation of Fully Homomorphic Encryption) originally set out to examine the security of FHE schemes, and the lattice hard problems on which they are based. This turned out to be relatively difficult, mainly due to the complexity of building software libraries which could support the advanced mathematics needed to perform experiments on modern multi-core processors. For example the NTL library is now (2015) able to support multi - threaded applications, but only if used with bleeding edge compilers on the latest Intel hardware. Thus the initial plan was perhaps a little ahead of its time. However, by leveraging additional sources of funding; most notably from the UKs EPSRC and the EUs ERC, the Bristol team was able to make substantial headway in other areas related to the PROCEED program which were not originally envisaged. These are centred around; General techniques for Fully Homomorphic Encryption Practical methods for actively secure Multi -Party Computation General theory behind Multi -Party Computation. This report documents the progress made by the Bristol team.
A modified method for multiple actuator input shaping

Input shaping is a feedforward technique that is used to eliminate system vibration. It is commonly a set of impulses that is convolved with the input. Typically the constraints used to design a shaper are dependent solely upon the frequency and damping of the system. When a system contains multiple inputs and multiple modes, only using the frequency and damping of the system requires one to use the same input shaper for each input. When considering that each input affects the system differently, it could be possible to develop a set of input shapers using the input parameters that would be of shorter duration and have fewer impulses. A multi-input, multi-mode technique has recently been developed which uses the input parameters of a system. The goal of this paper is to introduce a modified approach to this method that yields shorter shapers, leading to faster manoeuvres.
An Approach for Mining Web Service Composition Patterns from Execution Logs

A service-oriented application is composed of several web services to provide complex functionality that a single web service cannot provide. A set of services along with their control flows can be frequently used in multiple applications. Such services form a service composition pattern which is well tested in the numerous adoptions. Reusing service composition patterns in service composition provides an efficient way to improve the quality of new applications. To facilitate the documentation of service composition patterns, we propose an approach to automatically recognize service composition patterns from various applications. We identify service composition patterns by locating a set of associated services commonly used by different applications and recovering the control flows among the set of associated services.
Enhanced Face Liveness Detection Based on Features From Nonlinear Diffusion Using Specialized Deep Convolution Network And Its Application In OAuth

The major contribution of this research is the development of enhanced algorithms that will prevent face spoofing attacks by utilizing a single image captured from a 2-D printed image or a recorded video. We first apply a nonlinear diffusion based on an additive operator splitting (AOS) scheme with a large time step to acquire a diffused image. The AOS-based scheme enables fast diffusion that successfully reveals the depth information and surface texture in the input image. Then a specialized deep convolution neural network is developed that can extract the discriminative and high-level features of the input diffused image to differentiate between a fake face and a real face. Our proposed method yields higher accuracy as compared to the previously implemented state-of-theart methods. As an application of the face liveness detection, we develop face biometric authentication in an Open Authorization (OAuth) framework for controlling secure access to web resources. We implement a complete face verification system that consists of face liveness detection followed by face authentication that uses Local Binary Pattern