Hand gesture recognition using Leap Motion via deterministic learning

With the development of multimedia technology, traditional interactive tools, such as mouse and keyboard, cannot satisfy users’ requirements. Touchless interaction has received considerable attention in recent years with benefit of removing barriers of physical contact. Leap Motion is an interactive device which can be used to collect information of dynamic hand gestures, including coordinate, acceleration and direction of fingers. The aim of this study is to develop a new method for hand gesture recognition using jointly calibrated Leap Motion via deterministic learning. Hand gesture features representing hand motion dynamics, including spatial position and direction of fingers, are derived from Leap Motion. Hand motion dynamics underlying motion patterns of different gestures which represent Arabic numbers (0-9) and capital English alphabets (A-Z) are modeled by constant radial basis function (RBF) neural networks. Then, a bank of estimators is constructed by the constant RBF networks. By comparing the set of estimators with a test gesture pattern, a set of recognition errors are generated. The average L₁ norms of the errors are taken as the recognition measure according to the smallest error principle. Finally, experiments are carried out to demonstrate the high recognition performance of the proposed method. By using the 2-fold, 10-fold and leave-one-person-out cross-validation styles, the correct recognition rates for the Arabic numbers are reported to be 94.2%, 95.1% and 90.2%, respectively, for the English alphabets are reported to be 89.2%, 92.9% and 86.4%, respectively.     

A comparative study of dictionaries and corpora as methods for language resource addition

In this paper, we investigate the relative effect of two strategies for language resource addition for Japanese morphological analysis, a joint task of word segmentation and part-of-speech tagging. The first strategy is adding entries to the dictionary and the second is adding annotated sentences to the training corpus. The experimental results showed that addition of annotated sentences to the training corpus is better than the addition of entries to the dictionary. In particular, adding annotated sentences is especially efficient when we add new words with contexts of several real occurrences as partially annotated sentences, i.e. sentences in which only some words are annotated with word boundary information. According to this knowledge, we performed real annotation experiments on invention disclosure texts and observed word segmentation accuracy. Finally we investigated various language resource addition cases and introduced the notion of non-maleficence, asymmetricity, and additivity of language resources for a task. In the WS case, we found that language resource addition is non-maleficent (adding new resources causes no harm in other domains) and sometimes additive (adding new resources helps other domains). We conclude that it is reasonable for us, NLP tool providers, to distribute only one general-domain model trained from all the language resources we have.     

Stemming Hausa text: using affix-stripping rules and reference look-up

Stemming is a process of reducing a derivational or inflectional word to its root or stem by stripping all its affixes. It is been used in applications such as information retrieval, machine translation, and text summarization, as their pre-processing step to increase efficiency. Currently, there are a few stemming algorithms which have been developed for languages such as English, Arabic, Turkish, Malay and Amharic. Unfortunately, no algorithm has been used to stem text in Hausa, a Chadic language spoken in West Africa. To address this need, we propose stemming Hausa text using affix-stripping rules and reference lookup. We stemmed Hausa text, using 78 affix stripping rules applied in 4 steps and a reference look-up consisting of 1500 Hausa root words. The over-stemming index, under-stemming index, stemmer weight, word stemmed factor, correctly stemmed words factor and average words conflation factor were calculated to determine the effect of reference look-up on the strength and accuracy of the stemmer. It was observed that reference look-up aided in reducing both over-stemming and under-stemming errors, increased accuracy and has a tendency to reduce the strength of an affix stripping stemmer. The rationality behind the approach used is discussed and directions for future research are identified.     

TOWARD A MORE GLOBAL AND COHERENT SEGMENTATION OF TEXTS

The automatic text segmentation task consists of identifying the most important thematic breaks in a document in order to cut it into homogeneous passages. Text segmentation has motivated a large amount of research. We focus here on the statistical approaches that rely on an analysis of the distribution of the words in the text. Usually, the segmentation of texts is realized sequentially on the basis of very local clues. However, such an approach prevents the consideration of the text in a global way, particularly concerning the granularity degree adopted for the expression of the different topics it addresses. We thus propose here two new segmentation algorithms—ClassStruggle and SegGen—which use criteria rendering global views of texts. ClassStruggle is based on an initial clustering of the sentences of the text, thus allowing the consideration of similarities within a group rather than individually. It relies on the distribution of the occurrences of the members of each class ¹ In the following, the terms class and cluster indifferently refer to the same concept: a group of sentences. to segment the texts. SegGen proposes to evaluate potential segmentations of the whole text thanks to a genetic algorithm. It attempts to find a solution of segmentation optimizing two criteria, the maximization of the internal cohesion of the segments and the minimization of the similarity between adjacent ones. According to experimental results, both approaches appear to be very competitive compared to existing methods.     

Experience Learning From Basic Patterns for Efficient Robot Navigation in Indoor Environments

In this paper we propose a machine learning technique for real-time robot path planning for an autonomous robot in a planar environment with obstacles where the robot possess no a priori map of its environment. Our main insight in this paper is that a robot’s path planning times can be significantly reduced if it can refer to previous maneuvers it used to avoid obstacles during earlier missions, and adapt that information to avoid obstacles during its current navigation. We propose an online path planning algorithm called LearnerRRT that utilizes a pattern matching technique called Sample Consensus Initial Alignment (SAC-IA) in combination with an experience-based learning technique to adapt obstacle boundary patterns encountered in previous environments to the current scenario followed by corresponding adaptations in the obstacle-avoidance paths. Our proposed algorithm LearnerRRT works as a learning-based reactive path planning technique which enables robots to improve their overall path planning performance by locally improving maneuvers around commonly encountered obstacle patterns by accessing previously accumulated environmental information. We have conducted several experiments in simulations and hardware to verify the performance of the LearnerRRT algorithm and compared it with a state-of-the-art sampling-based planner. LearnerRRT on average takes approximately 10% of the planning time and 14% of the total time taken by the sampling-based planner to solve the same navigation task based on simulation results and takes only 33% of the planning time, 46% of total time and 95% of total distance compared to the sampling-based planner based on our hardware results.     

APIReal: an API recognition and linking approach for online developer forums

When discussing programming issues on social platforms (e.g, Stack Overflow, Twitter), developers often mention APIs in natural language texts. Extracting API mentions from natural language texts serves as the prerequisite to effective indexing and searching for API-related information in software engineering social content. The task of extracting API mentions from natural language texts involves two steps: 1) distinguishing API mentions from other English words (i.e., API recognition), 2) disambiguating a recognized API mention to its unique fully qualified name (i.e., API linking). Software engineering social content lacks consistent API mentions and sentence writing format. As a result, API recognition and linking have to deal with the inherent ambiguity of API mentions in informal text, for example, due to the ambiguity between the API sense of a common word and the normal sense of the word (e.g., append, apply and merge), the simple name of an API can map to several APIs of the same library or of different libraries, or different writing forms of an API should be linked to the same API. In this paper, we propose a semi-supervised machine learning approach that exploits name synonyms and rich semantic context of API mentions for API recognition in informal text. Based on the results of our API recognition approach, we further propose an API linking approach leveraging a set of domain-specific heuristics, including mention-mention similarity, scope filtering, and mention-entry similarity, to determine which API in the knowledge base a recognized API actually refers to. To evaluate our API recognition approach, we use 1205 API mentions of three libraries (Pandas, Numpy, and Matplotlib) from Stack Overflow text. We also evaluate our API linking approach with 120 recognized API mentions of these three libraries.     

Symmetric Masks for In-fill Pixel Interpolation on Discrete <Emphasis Type="Italic">p</Emphasis>:<Emphasis Type="Italic">q</Emphasis> Lattices

A 2D p:q lattice contains image intensity entries at pixels located at regular, staggered intervals that are spaced p rows and q columns apart. Zero values appear at all other intermediate grid locations. We consider here the construction, for any given p:q, of convolution masks to smoothly and uniformly interpolate values across all of the intermediate grid positions. The conventional pixel-filling approach is to allocate intensities proportional to the fractional area that each grid pixel occupies inside the boundaries formed by the p:q lines. However, these area-based masks have asymmetric boundaries, flat interior values and may be odd or even in size. Where edges, lines or points are in-filled, area-based p:q masks imprint intensity patterns that recall p:q because the shape of those masks is asymmetric and depends on p:q. We aim to remove these “memory” artefacts by building symmetric p:q masks. We show here that smoother, symmetric versions of such convolution masks exist. The coefficients of the masks constructed here have simple integer values whose distribution is derived purely from symmetry considerations. We have application for these symmetric interpolation masks as part of a precise image rotation algorithm which disguises the rotation angle, as well as to smooth back-projected values when performing discrete tomographic image reconstruction.     

Cooperation in Games and Epistemic Readings of Independence-Friendly Sentences

In the literature on logics of imperfect information it is often stated, incorrectly, that the Game-Theoretical Semantics of Independence-Friendly (IF) quantifiers captures the idea that the players of semantical games are forced to make some moves without knowledge of the moves of other players. We survey here the alternative semantics for IF logic that have been suggested in order to enforce this “epistemic reading” of sentences. We introduce some new proposals, and a more general logical language which distinguishes between “independence from actions” and “independence from strategies”. New semantics for IF logic can be obtained by choosing embeddings of the set of IF sentences into this larger language. We compare all the semantics proposed and their purported game-theoretical justifications, and disprove a few claims that have been made in the literature.     

Automatic hair extraction from 2D images

Automatic hair extraction from a given 2D image has been a challenging problem for a long time, especially when complex backgrounds and a wide variety of hairstyles are involved. This paper has made its contribution in the following three aspects. First, it proposes a novel framework that successfully combines the techniques of face detection, outlier-aware initial stroke placement and matting to extract the desired hair region from an input image. Second, it introduces an alpha space to facilitate the choice of matting parameters. Third, it defines a new comparison metric that is well suited for the alpha matte comparison. Our results show that, compared with the manually drawn trimaps for hair extraction, the proposed automatic algorithm can achieve about 86.2 % extraction accuracy.     

ChangeLocator: locate crash-inducing changes based on crash reports

Software crashes are severe manifestations of software bugs. Debugging crashing bugs is tedious and time-consuming. Understanding software changes that induce a crashing bug can provide useful contextual information for bug fixing and is highly demanded by developers. Locating the bug inducing changes is also useful for automatic program repair, since it narrows down the root causes and reduces the search space of bug fix location. However, currently there are no systematic studies on locating the software changes to a source code repository that induce a crashing bug reflected by a bucket of crash reports. To tackle this problem, we first conducted an empirical study on characterizing the bug inducing changes for crashing bugs (denoted as crash-inducing changes). We also propose ChangeLocator, a method to automatically locate crash-inducing changes for a given bucket of crash reports. We base our approach on a learning model that uses features originated from our empirical study and train the model using the data from the historical fixed crashes. We evaluated ChangeLocator with six release versions of Netbeans project. The results show that it can locate the crash-inducing changes for 44.7%, 68.5%, and 74.5% of the bugs by examining only top 1, 5 and 10 changes in the recommended list, respectively. It significantly outperforms the existing state-of-the-art approach.     

Algorithms for the Orthographic-<Emphasis Type="Italic">n</Emphasis>-Point Problem

We examine the orthographic-n-point problem (OnP), which extends the perspective-n-point problem to telecentric cameras. Given a set of 3D points and their corresponding 2D points under orthographic projection, the OnP problem is the determination of the pose of the 3D point cloud with respect to the telecentric camera. We show that the OnP problem is equivalent to the unbalanced orthogonal Procrustes problem for non-coplanar 3D points and to the sub-Stiefel Procrustes problem for coplanar 3D points. To solve the OnP problem, we apply existing algorithms for the respective Procrustes problems and also propose novel algorithms. Furthermore, we evaluate the algorithms to determine their robustness and speed and conclude which algorithms are preferable in real applications. Finally, we evaluate which algorithm is most suitable as a minimal solver in a RANSAC scheme.     

A text representation model using Sequential Pattern-Growth method

Text representation is an essential task in transforming the input from text into features that can be later used for further Text Mining and Information Retrieval tasks. The commonly used text representation model is Bags-of-Words (BOW) and the N-gram model. Nevertheless, some known issues of these models, which are inaccurate semantic representation of text and high dimensionality of word size combination, should be investigated. A pattern-based model named Frequent Adjacent Sequential Pattern (FASP) is introduced to represent the text using a set of sequence adjacent words that are frequently used across the document collection. The purpose of this study is to discover the similarity of textual pattern between documents that can be later converted to a set of rules to describe the main news event. The FASP is based on the Pattern-Growth’s divide-and-conquer strategy where the main difference between FASP and the prior technique is in the Pattern Generation phase. This approach is tested against the BOW and N-gram text representation model using Malay and English language news dataset with different term weightings in the Vector Space Model (VSM). The findings demonstrate that the FASP model has a promising performance in finding similarities between documents with the average vector size reduction of 34% against the BOW and 77% against the N-gram model using the Malay dataset. Results using the English dataset is also consistent, indicating that the FASP approach is also language independent.     

Almost cover-free codes

We say that an s-subset of codewords of a code X is (s, l)-bad if X contains l other codewords such that the conjunction of these l words is covered by the disjunction of the words of the s-subset. Otherwise, an s-subset of codewords of X is said to be (s, l)-bad. A binary code X is called a disjunctive (s, l) cover-free (CF) code if X does not contain (s, l)-bad subsets. We consider a probabilistic generalization of (s, l) CF codes: we say that a binary code is an (s, l) almost cover-free (ACF) code if almost all s-subsets of its codewords are (s, l)-good. The most interesting result is the proof of a lower and an upper bound for the capacity of (s, l) ACF codes; the ratio of these bounds tends as s→∞ to the limit value log₂ e/(le).     

Bounds on the rate of separating codes

A code with words in a finite alphabet is said to be an (s, l) separating code if for any two disjoint collections of its words of size at most s and l, respectively, there exists a coordinate in which the set of symbols of the first collection do not intersect the set of symbols of the second. The main goal of the paper is obtaining new bounds on the rate of (s, l) separating codes. Bounds on the rate of binary (s, l) separating codes, the most important for applications, are studied in more detail. We give tables of numerical values of the best presently known bounds on the rate.     

On Intertext in Chemotherapy: an Ethnography of Text in Medical Practice

Building on literary theory and data from a field study of text in chemotherapy, this article introduces the concept of intertext and the associated concepts of corpus and intertextuality to CSCW. It shows that the ensemble of documents used and produced in practice can be said to form a corpus of written texts. On the basis of the corpus, or subsections thereof, the actors in cooperative work create intertext between relevant (complementary) texts in a particular situation, for a particular purpose. The intertext of a particular situation can be constituted by several kinds of intertextuality, including the complementary type, the intratextual type and the mediated type. In this manner the article aims to systematically conceptualise cooperative actors’ engagement with text in text-laden practices. The approach is arguably novel and beneficial to CSCW. The article also contributes with a discussion of computer enabling the activity of creating intertext. This is a key concern for cooperative work as intertext is central to text-centric work practices such as healthcare.     

3D finger tracking and recognition image processing for real-time music playing with depth sensors

In this research, we propose a state-of-the-art 3D finger gesture tracking and recognition method. We use the depth sensors for both hands in real time music playing. In line with the development of 3D depth cameras, we implemented a set of 3D gesture-based instruments, such as Virtual Cello and Virtual Piano, which need precise finger tracking in 3D space. For hands tracking, model-based tracking for left hand and appearance-based tracking for right hand techniques are proposed. To detect finger gestures, our approaches consist number of systematic steps as reducing noise in depth map and geometrical processing for Virtual Cello. For Virtual Piano, we introduce the Neural Network (NN) method to detect special hand gestures. It has Multilayer Perceptron (MLP) structure with back propagation training. Literature has few examples using touch screen as medium, with fixed-coordinates, and 2D–gestures to control MIDI input. The end users should no longer carry anything on their hands. We use Senz3D and Leap Motion due to a few technical benefits. Senz3D and Leap Motion use a closer distance to hands, thus detailed finger gestures can be precisely identified. In the past years, we announced a set of virtual musical instruments and the MINE Virtual Band. Our research work is tested on lab environment and professional theatrical stage. More information and demonstrations of the proposed method can be accessed at: http://video.minelab.tw/DETS/VMIB/.     

Hydrodynamic focusing and interdistance control of particle-laden flow for microflow cytometry

Single-file focusing and minimum interdistance of micron-size objects in a sample is a prerequisite for accurate flow cytometry measurements. Here, we report analytical models for predicting the focused width of a sample stream b as a function of channel aspect ratio α, sheath-to-sample flow rate ratio f and viscosity ratio λ in both 2D and 3D focusing. We present another analytical model to predict spacing between an adjacent pair of objects in a focused sample stream as a function of sample concentration C, mobility ? of the objects in the prefocused and postfocused regions and flow rate ratio f in both 2D and 3D flow focusing. Numerical simulations are performed using Ansys Fluent VOF model to predict the width of sample stream in 2D and 3D hydrodynamic focusing for different sample-to-sheath viscosity ratios, aspect ratios and flow rate ratios. Experiments are performed on both planar and three-dimensional devices fabricated in PDMS to demonstrate focusing of sample stream and spacing of polystyrene beads in the unfocused and focused stream at different sample concentrations C. The predictions of the analytical model and simulations are compared with experimental data, and a good match is found (within 12 %). Further, mobility of objects is experimentally studied in 2D and 3D focusing, and the spread of the mobility data is used as tool for the demonstration of particle focusing in flow cytometer applications.     

Assessment of crowdsourcing and gamification loss in user-assisted object segmentation

There has been a growing interest in applying human computation – particularly crowdsourcing techniques – to assist in the solution of multimedia, image processing, and computer vision problems which are still too difficult to solve using fully automatic algorithms, and yet relatively easy for humans. In this paper we focus on a specific problem – object segmentation within color images – and compare different solutions which combine color image segmentation algorithms with human efforts, either in the form of an explicit interactive segmentation task or through an implicit collection of valuable human traces with a game. We use Click’n’Cut, a friendly, web-based, interactive segmentation tool that allows segmentation tasks to be assigned to many users, and Ask’nSeek, a game with a purpose designed for object detection and segmentation. The two main contributions of this paper are: (i) We use the results of Click’n’Cut campaigns with different groups of users to examine and quantify the crowdsourcing loss incurred when an interactive segmentation task is assigned to paid crowd-workers, comparing their results to the ones obtained when computer vision experts are asked to perform the same tasks. (ii) Since interactive segmentation tasks are inherently tedious and prone to fatigue, we compare the quality of the results obtained with Click’n’Cut with the ones obtained using a (fun, interactive, and potentially less tedious) game designed for the same purpose. We call this contribution the assessment of the gamification loss, since it refers to how much quality of segmentation results may be lost when we switch to a game-based approach to the same task. We demonstrate that the crowdsourcing loss is significant when using all the data points from workers, but decreases substantially (and becomes comparable to the quality of expert users performing similar tasks) after performing a modest amount of data analysis and filtering out of users whose data are clearly not useful. We also show that – on the other hand – the gamification loss is significantly more severe: the quality of the results drops roughly by half when switching from a focused (yet tedious) task to a more fun and relaxed game environment.     

A temporal modal defeasible logic for formalizing social commitments in dialogue and argumentation models

In this paper, we extend a temporal defeasible logic with a modal operator Committed to formalize commitments that agents undertake as a consequence of communicative actions (speech acts) during dialogues. We represent commitments as modal sentences. The defeasible dual of the modal operator Committed is a modal operator called Exempted. The logical setting makes the social-commitment based semantics of speech acts verifiable and practical; it is possible to detect if, and when, a commitment is violated and/or complied with. One of the main advantages of the proposed system is that it allows for capturing the nonmonotonic behavior of the commitments induced by the relevant speech acts.