Miracle-dz/UniFAR-FTUs huggingface dataset:
Using iPads and Mobile Technology for Children with Developmental Disabilities: Facilitating Language and Literacy Development

With increasing access to iPads and mobile technology in both home and school settings, evidence regarding how best to use this technology to enhance language and literacy learning is lacking, particularly for children with developmental disabilities. As a comprehensive review, this chapter discusses the use of iPads and mobile technology in the language and literacy development of this population. It concludes that while iPads and mobile technology provide opportunities for language and literacy development, the inherent challenges and limitations of this technology warrant attention from parents, educators and speech-language pathologists. iPads and mobile technology may be a valuable accelerator for the language and literacy development of children with developmental disabilities if used properly; however, improper or careless usage can become a distraction that further delays the communication development of this population.
Blur adaptive sparse representation of random patches for race recognition on blurred images

Face recognition in unconstraint environments is an active research area on forensics. The challenge is to deal with real world environments in order to achieve a high recognition rate under difficult conditions such as changes in ambient lightning, pose, expressions, occlusion, face size, blurriness, among others. In this research, we address one of the biggest problems: face recognition in blurred images.
Efficient Procedural Generation of Forests

Procedural methods offer an automated means of generating complex cityscapes, incorporating the placement of park areas and the layout of roads, plots and buildings. Unfortunately, existing interfaces to procedural city systems tend to either focus on a single aspect of city layout (such as the road network) ignoring interaction with other elements (such as building dimensions) or expect numeric input with little visual feedback, short of the completed city, which may take up to several minutes to generate. In this paper we present an interface to procedural city generation, which, through a combination of sketching and gestural input, enables users to specify different land usage (parkland, commercial, residential and industrial), and control the geometric attributes of roads, plots and buildings. Importantly, the inter-relationship of these elements is pre-visualized so that their impact on the final city layout can be predicted. Once generated, further editing, for instance shaping the city skyline or redrawing individual roads, is supported. In general, City Sketching provides a powerful and intuitive interface for designing complex urban layouts.
Bit-allocation and rate-control algorithm for stereo video coding

A new bit-allocation and rate-control algorithm for stereo video coding was proposed to improve its coding efficiency and stream reliability.The conventional quadric rate-distortion model was improved and a new one was introduced by incorporating the human visual system(HVS) characteristics.Experimental results show that the improved new rate-distortion model is more precise than the conventional one and the proposed new algorithm can efficiently implement bit allocation and rate control according to coding parameters.
The animation forming mechanism in VR-based Virtual Laboratory

Animation is an important presentation method in the Internet-based Virtual Reality Laboratory (iVRLab). Because of the iVRLab's four basic features - 3D form, interactivity, network based working and virtuality, conventional animation methods cannot meet the requirement. Based on the VRML animating principle and analysis of the iVRLab's requirement, a brand-new animation forming mechanism is presented. An example of liquid surface lifting animation is illustrated to give more details.
AMIDST: Analysis of MassIve Data STreams

The Analysis of MassIve Data STreams (AMIDST) Java toolbox provides a collection of scalable and parallel algorithms for inference and learning of hybrid Bayesian networks from data streams. The toolbox, available at http://amidst.github. io/toolbox/ under the Apache Software License version 2.0, also efficiently leverages existing functionalities and algorithms by interfacing to software tools such as HUGIN and MOA.
Query Disambiguation from Web Search Logs

Query disambiguation problem is getting more attention as the data available on the web grows exponentially. To address this problem, we present a system that extracts relevant categories as the user's intention in search. To predict the categories of a given user input query, we employed machine learning algorithms that learn the correlation between the query categories and the user search history.
TAGPRO: UN SISTEMA PER IL POS- TAGGING DELL'ITALIANO BASATO SU SVM TAGPRO: A SYSTEM FOR ITALIAN POS TAGGING BASED ON SVM

Part of speech tagging is the problem of determining the correct parts of speech of a sequence of words. The most frequently applied approaches for this task are based on machine learning: Hidden Markov Models [1], Maximum Entropy taggers [5], Transformation-based learning, Memory Based learning [2], Decision Trees [3] and Support Vector Machines (SVMs) [4]. SVMs are among the most widely used techniques, and various implementations are available. As argued by T. Joachims [6], one of advantages of SVMs is that dimensionality reduction is usually not needed, as they are robust to overfitting and scale up well to high feature dimensions. We used YAMCHA, an SVM-based machine learning environment [8], to build TagPro, a PoS-tagging system exploiting a rich set of linguistic features, such as morphological analysis and proper name gazetteers. TagPro is part of TextPro, a suite of NLP tools developed at FBK-irst, which includes MorphoPro, a morphological analyzer that provides the morphological analysis exploited by TagPro. TagPro was trained on the EVALITA development set, using the standard EAGLES tagset and a new, structurally different, tagset (DISTRIB). In the rest of the paper we give further details on SVMs, the feature space that we used, and the results we obtained.
Isospectral Manifold Learning Algorithm

Manifold learning based on spectral method has been widely used recently for discovering a low-dimensional representation in the high-dimensional vector space. Isospectral manifold learning is one of the main contents of spectrum method. Isospectral manifold learning stems from the conclusions that if only the spectrums of manifold are the same, so are their internal structures. However, the difficult task about the calculation of the spectrum is how to select the optimal neighborhood size and construct reasonable neighboring weights. In this paper, a supervised technique called isospectral manifold learning algorithm(IMLA) is proposed. By modifying directly sparse reconstruction weight, IMLA takes into account the within-neighboring information and between-neighboring information. Thus, it not only preserves the sparse reconstructive relationship, but also sufficiently utilizes discriminant information. Compared with PCA and other algorithms, IMLA has obvious advantages. Experimental results on face databases(Yale, ORL and Extended Yale B) show the effectiveness of the IMLA method.
Multi-session Network Coding characterization using new linear coding frameworks

Han, Jaemin Ph.D., Purdue University, May 2016. Multi-session Network Coding Characterization using New Linear Coding Frameworks. Major Professor: ChihChun Wang. Recently, Network Coding (NC) has emerged as a promising technique in modern communication networks and has shown extensive potentials in practical implementations and theoretical developments. Nevertheless, the NC problem itself remains largely open especially where the multiple flows (sessions) exist. Unlike single-session where all receivers want the same information, they demand different set of information in multi-session and thus NC strategy should be carefully designed to avoid interferences. However, characterizing an optimal strategy (even a simple solution) has known to be of prohibitive complexity even we restrict to the linear network coding (LNC) problem. This thesis provides a fundamental approach to overcome this multi-session complexity. We first consider the Directed Acyclic Integer-Capacity network model that characterizes the real-life instantaneous Wireline Networks. In this model, people recently applied the results of wireless interference channels to evade the multisession difficulties. However, our NC understanding is still nascent due to different wireline channel characteristics to that of wireless. Therefore, motivated by the graph-theoretic characterizations of classic linear NC results, we first propose a new Precoding-based Framework and its fundamental properties that can bridge between the point-to-point network channel and the underlying graph structures. Such relationships turn out to be critical when characterizing graph-theoretically the feasibility of the Precoding-based solutions. One application of our results is to answer the
Engineering Drawing Networked Exercise Book and Correction System

Analysing the basic function of the engineering drawing networked exercise book and intelligent correction system, introducing the interface and the construction of the system, analysing the database designing of the system, and giving a example of intelligent correction. User can log in the romote server on client computer, download the exercise, do exercise in the AutoCAD, instantly send the completed exercise to the server computer, and view the corrected result.
Adaptive Interoperable Models of All Things Based on Human Language

The ultimate goal of intelligent technologies is to fully understand human speech. Therefore, their ideal models of real things must be based on human language. We will introduce our Universal Modeling System (UMS) with which we constructed 4,000 models of all things in the dictionary from the sound symbolism of their names. We integrated them in a software system called Readware to test their intelligence, interoperability and relevance. Readware outperformed all global participants in a US government evaluation (TREC Conference, 2000) of question-answering systems. We will present example models from twelve fields (cognition, psychology, biology, sociology, etc.). We hope to openly share UMS with others to create new systems.
Feature Extraction and Classification of EEG in Online Brain-Computer Interface

In the study of brain-computer interface(BCI),a novel method of extracting electroencephalography(EEG) features based on discrete wavelet transform(DWT) and autoregressive(AR) model was proposed.First,the EEG signal was decomposed to three levels by Daubechies wavelet function and statistics of wavelet coefficients were computed.Also,the sixth-order AR coefficients of the EEG signal were estimated using Burg's algorithm.Then,the combination features were used as an input vector for neural network(NN) classifier,support vector machine(SVM) classifier,and linear discriminant analysis(LDA) classifier.Performance of this feature extraction method was tested using the data set from BCI 2003 competition.The recognition rate was compared with the best result of the competition and the classification results showed the effectiveness of this algorithm.Moreover,applying this pattern recognition algorithm to online robot control system based on EEG,the average accuracy of 89.5% was obtained.This method provides a new idea for the study of online BCI system.
D1.8.1 Base upper-level ontology (BULO) Guidance 1

An important practical approach to ontology generation is the use of background or pre-existing knowledge in the form of a basic upper-level ontology. Such an ontology can also be used for metadata generation and as a groundwork for the overall knowledge modelling and integration strategy of a KM environment. The essential contribution of this deliverable is a basic upper-level ontology called PROTON (PROTo ONtology), which is hereby introduced and documented. It contains about 300 classes and 100 properties, providing coverage of the general concepts necessary for a wide range of tasks, including semantic annotation, indexing, and retrieval of documents. The design principles can be summarized as follows (i) domain-independence; (ii) light-weight logical definitions; (iii) alignment with popular standards; (iv) good coverage of named entities and concrete domains (i.e. people, organizations, locations, numbers, dates, addresses). The ontology is originally encoded in a fragment of OWL Lite and split into four modules: System, Top, Upper, and KM (Knowledge Management).
High Efficient Extraction of Tryptophan Using Deep Eutectic Solvent-based Aqueous Biphasic Systems

Deep eutectic solvent-based aqueous biphasic systems were established as greener replacements of organic solvents for the extraction of amino acid in this work. Choline chloride/polyethylene glycol 2000-based deep eutectic solvent was synthesized and characterized by element analysis, thermogravimetric analysis, and Fourier-transform infrared spectroscopy. The phase diagrams of deep eutectic solvent+salt (Na3C6H5O7/Na2CO3/NaH2PO4/K2HPO4)+H2O aqueous biphasic systems were determined at 298 K and the aqueous biphasic systems were applied to extract the tryptophan for the first time. The extraction conditions of amino acid concentration, deep eutectic solvent amount, salt dosage, and the extraction temperature were investigated and optimized. Under the optimum conditions, the extraction efficiency of tryptophan can reach up to 86.72-93.88 % using deep eutectic solvent-based aqueous biphasic systems with the salt of Na2CO3, Na3C6H5O7, NaH2PO4 or K2HPO4. UV/Vis and fluorescence spectra confirmed that the conformation of tryptophan did not change after extraction. Deep eutectic solvent-based aqueous biphasic systems have great potential application in highly efficient extraction of biologically relevant amino acids and other substances.
Unified type checking for type classes and type families

Haksell has a rich type system with various complementary, interacting and overlapping features. In particular we think of the established type classes, with several extensions: multiple parameters, functional dependencies, . In a recent proposal, a new feature is added to the Haskell language: type-level functions, or type families in GHC. This multitude of type-level features is a blessing for programmers. A well-chosen combination of features allows the accurate expression of many problem domain semantics. However, the plethora of features is also a nightmare for Haskell compiler writers, who have to implement and maintain all these features. For instance, GHC’s core type checking modules for type classes and type families comprise approximately 3,1 kloC and 1,2 kloc, which is understood by very few people. Currently, no other Haskell system has managed to provide the same type class functionality as GHC. The contributions of this work aim at recuding the implementation complexity of two Haskell type system features, type classes and type families:
An image authentication method using digital watermarking

We consider the problem of digital watermarking for credibility authentication or tamper proofing of multimedia. We specifically propose a fragile watermarking algorithm for complete tamper proofing of a still image, which can be applied to authenticate courtroom evidence, insurance claims, and journalistic photography. Based on the integer wavelet transform, the algorithm embeds the watermark bit stream in the discrete wavelet domain of the image by quantizing the corresponding coefficients. Also, this approach provides information on specific frequencies and localized spatial regions of the image that has been modified.
Research and Discussion on Teaching Model of Data Structure Based on PBL

Data Structure course is a core course of computer specialty, it plays an important role in the whole computer specialized curriculum sys-tem. The problem of single teaching method, student learning difficulties, low efficiency of teaching has always been in the teaching of da-ta structure. According to the characteristics of data structure course, proposes the new teaching method to combine traditional teaching with teaching in PBL. And practices have been done in the teaching of data structure. Practices prove that the new teaching method not only improves the teacher's teaching quality, but also cultivate the students' learning interest. And it also improves the students ‘ independent learning abilities.
Image Zooming by Combination of the Curvature-driven and Edge-stopping Nonlinear Diffusion

Based on the good performances of nonlinear diffusion in preserving important image features,the Curvature-driven with Edge-stopping(CE model) was proposed and applied into image zooming.The CE model has the protection of little curvature and big gradient is emphasized,at same time,it has two effects of connecting broken isophotes and strengthening edge.The condition of the initial and boundary value of image zooming,as well as numerical schemes,were further discussed.For image zooming,the CE model exhibits better performance than the nonlinear diffusion model.
Ways to retouch photos

PS is short for Photoshop,a photo-processing software developed by Adobe Systems Incorporated in early times.This paper does not intend to introduce Photoshop here because its huge system and great complexity in operation but another extremely simple and intuitive photo processing software–Meitu Xiuxiu,a user-friendly freeware developed by Meitu Net Technology Ltd.Common computer users usually like to use those softewares that easy to use.
Bipedal Walking for a Full Size Humanoid Robot Utilizing Sinusoidal Feet Trajectories and Its Energy Consumption

This research effort aims to develop a series of full-sized humanoid robots, and to research a simple but reliable bipedal walking method. Since the debut of Wabot from Waseda University in 1973, several full-sized humanoid robots have been developed around the world that can walk, and run. Although various humanoid robots have successfully demonstrated their capabilities, bipedal walking methods are still one of the main technical challenges that robotics researchers are attempting to solve. It is still challenging because most bipedal walking methods, including ZMP (Zero Moment Point) require not only fast sensor feedback, but also fast and precise control of actuators. For this reason, only a small number of research groups have the ability to create full-sized humanoid robots that can walk and run. However, if we consider this problem from a different standpoint, the development of a full-sized humanoid robot can be simplified as long as the bipedal walking method is easily formulated. Therefore, this research focuses on developing a simple but reliable bipedal walking method. It then presents the designs of two versions of a new class of super lightweight (less than 13 kg), full-sized (taller than 1.4 m) humanoid robots called CHARLI-L (Cognitive Humanoid Autonomous Robot with Learning Intelligence – Lightweight) and CHARLI-2. These robots have unique designs compared to other fullsized humanoid robots. CHARLI-L utilizes spring assisted parallel four-bar linkages with synchronized actuation to achieve the goals of lightweight and low cost. Based on the experience and lesions learned from CHARLI-L, CHARLI-2 uses gear train reduction mechanisms, instead of parallel four-bar linkages, to increase actuation torque at the joints while further reducing weight. Both robots successfully demonstrated untethered bipedal locomotion using an intuitive walking method with sinusoidal foot movement. This walking method is based on the ZMP method. Motion capture tests using six high speed infrared cameras validate the proposed bipedal walking method. Additionally, the total power and energy consumptions during walking are calculated from measured actuator currents.
Application of LabVIEW and MATLAB in ESPI image processing

In order to process conveniently speckle images in speckles measurement,a simple system of electronic speckle pattern interferometry images processing was designed by means of LabVIEW and MATLAB.In this method,LabVIEW was used to design display interface and the powerful image processing functions of MATLAB were invoked to process speckle images.The experimental result shows that the contrast of fringes can be increased by the proposed method.
Selective inductive transfer

Multi-target models, which predict multiple target variables simultaneously, may predict some of the targets more accurately, and other targets less accurately than a single-target model. This raises the question whether it is possible to find, for a given main target, a subset of the other targets that, when combined with the main target in a multi-target model, results in the most accurate model for the main target. We propose Selective Inductive Transfer, an algorithm that automatically finds such a subset.
Identifying Interface Design Patterns by Studying Intrinsic Designs

Designing an effective user interface for a new system is challenging even for the most experienced designer. In this study, we present a strategy to find a user interface design pattern for various problem domains through a systematic process. Our strategy arises from studying and analyzing several popular human-centered interface designs in their entirety of evolution. We apply our technique to redesign Walmart‟s online-store checkout web-forms, reduce their complexity by almost 80 percent, and greatly improve its performance without compromising its functionality.
Screen Collection and Transmitting Based on JMF

The screen record module which based on Java and JMF technology includes two sub-modules, such as screen capture and net transmission. The former used the Robot object which provided by JDK to capture screen continuously; then, then transmitted those continuous pictures into a movie and create data source from this movie. The latter used the RTP protocol to transmit media stream. In this system, the JMF API was used to read the information of files and create data source in a proper format. The RTP API was used for transmitting media stream in the network.
Cox-nnet: an artificial neural network method for prognosis prediction on high-throughput omics data

Artificial neural networks (ANN) are computing architectures with massively parallel interconnections of simple neurons and has been applied to biomedical fields such as imaging analysis and diagnosis. We have developed a new ANN framework called Cox-nnet to predict patient prognosis from high throughput transcriptomics data. In over 10 TCGA RNA-Seq data sets, Cox-nnet achieves a statistically significant increase in predictive accuracy, compared to the other three methods including Cox-proportional hazards (Cox-PH), Random Forests Survival and CoxBoost. Cox-nnet also reveals richer biological information, from both pathway and gene levels. The outputs from the hidden layer node can provide a new approach for survival-sensitive dimension reduction. In summary, we have developed a new method for more accurate and efficient prognosis prediction on high throughput data, with functional biological insights. The source code is freely available at github.com/lanagarmire/cox-nnet.