Miracle-dz/UniFAR-FTUs huggingface dataset:
USING A 3D TIME-OF-FLIGHT RANGE CAMERA FOR VISUAL TRACKING

Abstract We propose the usage of a novel 3D-Sensor for Visual Servoing in order to avoid the problem of depth estimation that is to be solved in most servoing schemes. The sensor used in this paper is based on the time-of-flight principle and returns a depth image along with an intensity image. As these are acquired by the same CCD array, no calibration between them is necessary. As an application example a position-based eye-in hand servoing scheme is presented with the sensor mounted on the end-effector of a pan-tilt unit. An experimental tracking task was defined as follows: a sphere moving with respect to the pan-tilt unit has to be kept in the optical centre of the camera. We implemented velocity-based control of the manipulator joints and obtained the following results: the pan-tilt unit was operated at a control frequeny of 17.5 Hz and — starting from a skewed pan and tilt — the sphere could be regulated to the optical center in less than one second.
Using Compiled Knowledge to Guide and Focus Abductive Diagnosis

Several artificial intelligence architectures and systems based on "deep" models of a domain have been proposed, in particular for the diagnostic task. These systems have several advantages over traditional knowledge based systems, but they have a main limitation in their computational complexity. One of the ways to face this problem is to rely on a knowledge compilation phase, which produces knowledge that can be used more effectively with respect to the original one. We show how a specific knowledge compilation approach can focus reasoning in abductive diagnosis, and, in particular, can improve the performances of AID, an abductive diagnosis system. The approach aims at focusing the overall diagnostic cycle in two interdependent ways: avoiding the generation of candidate solutions to be discarded a posteriori and integrating the generation of candidate solutions with discrimination among different candidates. Knowledge compilation is used off-line to produce operational (i.e., easily evaluated) conditions that embed the abductive reasoning strategy and are used in addition to the original model, with the goal of ruling out parts of the search space or focusing on parts of it. The conditions are useful to solve most cases using less time for computing the same solutions, yet preserving all the power of the model-based system for dealing with multiple faults and explaining the solutions. Experimental results showing the advantages of the approach are presented.
Fixed-Term Contracts and Employment Adjustment: An Empirical Test of the Coreperiphery Hypothesis Using German Establishment Data

Fixed-term contracts (FTCs), an important feature of the employment relationship of the peripheral workforce, are analysed to test the following two hypotheses, which are based on dual labour market theory: (i) Firms use FTCs for the peripheral workforce to adjust the level of employment to the profit maximising level in case of demand fluctuations. (ii) Thanks to the utilisation of FTCs, the core workforce is less exposed to employment adjustment. Both hypotheses are supported by the results of the econometric analysis, which uses a German establishment panel.
The object-oriented software development method: a practical approach to object-oriented development

The Object-Oriented Software Development Method (OOSD) includes object-oriented requirements analysis, as well as object-oriented design. OOSD is a practical method of developing a software system which focuses on the objects of a problem throughout development. OOSD's focus on objects early in the development, with attention to generating a useful model, creates a picture of the system that is modifiable, reusable, reliable, and understandable — the last perhaps most important because the picture of a software system created by a development method must be an effective device for communication among developers, customers, management, and quality-assurance personnel. Most object-oriented methods competing for the attention of the software developer actually apply traditional Structured Analysis (function-based), or variations of Structured Analysis, to requirements activity, and work through a transition process to an object-oriented design [1,2,7,10,11]. In these methods the developer begins with functionally-based requirements analysis, and only reaches an object-oriented design by the intermediary step of converting a traditional, functionally-decomposed data flow diagram (DFD) to an object-oriented DFD (or equivalent). In this conversion process, objects are identified through a set of heuristics which group “transformations” in the DFD generated during requirements analysis. These methods carry a number of interesting but unfortunate burdens. Lower-level objects, which directly relate to real-world objects, are easily identified, but higher-level objects are generally more arbitrary, so that developers do not consistently identify a hierarchy of objects which achieves significant improvement in software engineering goals (e.g., reliability, maintainability, reusability). The heuristics for identifying objects usually relate the DFD transforms to the object that controls execution of an operation, rather than the object which “owns” the operation. These methods generally ignore the need to convert behavior descriptions of the DFD transforms into behavior descriptions of the objects. Finally, the use of Structured Analysis in an otherwise object-oriented approach complicates the tracing of requirements by forcing the developer to look first to DFD transforms and their behavior descriptions, and then to the objects.
A Machine Learning Approach to German Pronoun Resolution

This paper presents a novel ensemble learning approach to resolving German pronouns. Boosting, the method in question, combines the moderately accurate hypotheses of several classifiers to form a highly accurate one. Experiments show that this approach is superior to a single decision-tree classifier. Furthermore, we present a standalone system that resolves pronouns in unannotated text by using a fully automatic sequence of preprocessing modules that mimics the manual annotation process. Although the system performs well within a limited textual domain, further research is needed to make it effective for open-domain question answering and text summarisation.
A Subquadratic Algorithm for Approximate Regular Expression Matching

Abstract The main result of this paper is an algorithm for approximate matching of a regular expression of size m in a text of size n in time O ( nm /log d +2 n ), where d is the number of allowed errors. This algorithm is the first o ( mn ) algorithm for approximate matching to regular expressions.
The Trend of Mandatory Vaccination among Children in Egypt

Increase of immunization coverage rate is the most effective method for decreasing the burden of childhood vaccine-preventable diseases. The objective of this work was to present an up-to-date review of the efficacy and the trend of mandatory vaccination regimens among children in Egypt, and its efficacy in decreasing the burden of vaccine-preventable diseases among children under 5 years in Egypt during the period 1995-2005. The sources of data depend on the bibliographic review on MEDLINE, published studies and reports, and WHO and EMRO databases that covered the period from 1995 to 2005. Statistic analysis of the available data revealed that the coverage rate of the existing vaccination program in Egypt has greatly expanded over years and is significantly associated with a swift reduction in the burden of the vaccine-preventable childhood diseases (measles, mumps, rubella, neonatal tetanus, pertussis, diphtheria, poliomyelitis, and hepatitis B). Varicella vaccine is strongly recommended to be added to the mandatory vaccination of children in Egypt.
Electrohydrodynamics: a study of collectivebehavior and self-organization of an oil-in-oilemulsion

In this thesis, I study the collective behavior and self-organization of immiscible silicone oil drops in a castor oil medium. Castor oil is a “leaky dielectric” and the silicone oil drops interact with each other due to electrohydrodynamic forces induced by an imposed electric field. The strength and the range of the hydrodynamic interactions are modulated by changing amplitude and frequency of the electric field, respectively, in a small capacitor. The result of the electrohydrodynamic forces is to induce flows that induce drop motions, deformations and breakup. I study the effect of cell thickness, d, on the size distribution and dynamics of silicone oil drops in presence of an external DC electric field. I also investigate the effect of dimensionality by varying the cell thickness, d, and observation of drop dynamics as well as the observation of an electrohydrodynamically driven convective instability. For the first time, to our knowledge, two-roll structures, with a lateral size that is half the cell thickness, are observed experimentally. Further, this instability is also seen in castor oil medium, in the absence of any liquid-liquid and solid-liquid interfaces, indicating the importance of electrokinetic effects. Next, I constrain the motion of silicone oil drops in 2D, using dielectrophoretic traps, in order to create a 2D droplet crystal. By driving this crystal with frequencytunable electrohydrodynamic forces, I construct a amplitude-frequency phase diagram for the non-equilibrium order to disorder phase transition of silicone oil drops in castor oil medium. The pure order-to-disorder behaviour is observed for a amplitudefrequency regime where no breakup events occur but the hydrodynamic flows are strong enough to deform and partially unpin the droplets from their trap potential. Finally, an examination of the underlying flows using tracer particles reveals anomalous superdiffusive motion with power law scaling of t3/2. The underlying probability distribution for these anomalous motions is non-Gaussian and has the form exp (-( x2 )δ/2 4Kγt3/2 At short times, it is a simple exponential decay (i.e. δ = 1), while at longer times the distribution is consistent with δ = 1.4. This system exhibits non-equilibrium self-organization that is frequency- and amplitude-tunable, that will not only allow more detailed comparisons with detailed theory and simulation in the future. Moreover, it has been demonstrated as a model system for studying self-organization with tunable hydrodynamic interactions.
Practical extensions of point labeling in the slider model

This paper extends research by the authors together with Alexander Wolff on point label placement using a model where labels can be placed at any position that touches the point (the slider model). Such models have been shown to perform better than methods that allow only a fixed number of positions per label. The novelties in this paper include respecting other map features that must be avoided by the labels, and incorporating labels with different height. The result is an efficient and simple O((n+m)log(n+m)) time algorithm with a performance guarantee for label placement in the slider model. Here n is the number of points to be labeled and m is the combinatorial complexity of the map features that must be avoided. Due to its efficiency, the algorithm can be used in interactive and on-line mapping.
Document Analysis System

This paper outlines the requirements and components for a proposed Document Analysis System, which assists a user in encoding printed documents for computer processing. Several critical functions have been investigated and the technical approaches are discussed. The first is the segmentation and classification of digitized printed documents into regions of text and images. A nonlinear, run-length smoothing algorithm has been used for this purpose. By using the regular features of text lines, a linear adaptive classification scheme discriminates text regions from others. The second technique studied is an adaptive approach to the recognition of the hundreds of font styles and sizes that can occur on printed documents. A preclassifier is constructed during the input process and used to speed up a well-known pattern-matching method for clustering characters from an arbitrary print source into a small sample of prototypes. Experimental results are included.
X and Y cells in the lateral geniculate nucleus of macaque monkeys.

1. Cells of the lateral geniculate nucleus (l.g.n.) in macaque monkeys were sorted into two functional groups on the basis of spatial summation of visually evoked neural signals. 2. Cells were called X cells if their responses to contrast reversal of fine sine gratings were at the fundamental temporal modulation frequency with null positions one quarter of a cycle away from positions for peak response. Cells were called Y cells if their responses to such stimuli were at twice the modulation frequency and were approximately independent of spatial phase. 3. Ninety‐nine percent of the cells in the four dorsal parvocellular layers of the l.g. n. were X cells; about seventy‐five percent of the cells in the two ventral magnocellular layers were also X cells. The remainder were Y cells. 4. We confirmed previous findings that magnocellular cells had a shorter latency of response to electrical stimulation of the optic chiasm. 5. Magnocellular cells had much higher contrast sensitivities than did parvocellular cells. 6. Therefore, two distinct classes of X cells exist in the macaque l.g. n. : parvocellular X cells and magnocellular X cells. The great difference in their properties suggests that they have different functions in vision. The Y cells in the magnocellular layers form a third functional group with spatial properties distinctly different from the X cells. 7. We propose that the magnocellular layers of the macaque monkey's l.g. n. may be homologous to the A and A1 layers of the cat's l.g. n.
The stochastic EM algorithm: estimation and asymptotic results

The EM algorithm is a much used tool for maximum likelihood estimation in missing or incomplete data problems. However, calculating the conditional expectation required in the E-step of the algorithm may be infeasible, especially when this expectation is a large sum or a high-dimensional integral. Instead the expectation can be estimated by simulation. This is the common idea in the stochastic EM algorithm and the Monte Carlo EM algorithm. In this paper some asymptotic results for the Stochastic EM algorithm are given, and estimation based on this algorithm is discussed. In particular, asymptotic equivalence of certain simple estimators is shown, and a simulation experiment is carried out to investigate this equivalence in small and moderate samples. Furthermore, some implementation issues and the possibility of allowing unidentified parameters in the algorithm are discussed.
A Methodology to Segment the Text for Index Terms

The problem of information overload is a hot issue with the growth of the world wide web. The need for the tools those should be able to absorb this huge information and eliminate this problem is evident especially for IR systems. Text is not a simple sequence of words but carries a structure. It is essential to handle these uncontrollable complex structures of sentence, grammatical and lexical irrelevancy of different units. The main idea to handle these problems is to segment the text into elementary units, which will be simpler and lesser complex than their equivalent text. We have used cue phrases, punctuations. We are presenting an algorithm, which is not only efficient but also handling more than 500 cue phrases and most of punctuations. This proposed algorithm can yield elementary units, which can be used by Rhetorical Relations Finder to get relations among them, which can be used by the RST Parser for the construction of RST Tree which will be used to design an RST based indexer. In future, the algorithm can be enhanced for handling other discourse markers, which will enable us to handle the most complex cases where cue phrases and punctuations are not applicable.
An Optimal Non-adaptive Search Plan for 3D Models Retrieval

In this paper, we present a non-adaptive optimal plan that minimizes the mean search efforts based on the optimal search theory. A non-adaptive plan makes no use of the feedback generated by the search except the query object is found. In particular, we apply our search algorithm on PSB database to retrieve different classes of 3D models. Experiment results showed that the retrieval time to find the query object can be reduced largely
Hierarchical community decomposition via oblivious routing techniques

The detection of communities in real-world large-scale complex networks is a fundamental step in many applications, such as describing community structure and predicting the dissemination of information. Unfortunately, community detection is a computationally expensive task. Indeed, many approaches with strong theoretic guarantees are infeasible when applied to networks of large scale. Numerous approaches have been designed to scale community detection algorithms, many of which leverage local optimizations or local greedy decisions to iteratively find the communities. Solely relying on local techniques to detect communities, rather than a global objective function, can fail to detect global structure of the network. In this work, we instead formulate a notion of a hierarchical community decomposition (HCD), which takes a more global view of hierarchical community structure. Our main contributions are as follows. We formally define a (λ, delta)-HCD where λ parametrizes the connectivity within each sub-community at the same hierarchical level and δ parametrizes the relationship between communities across two consecutive levels. Based on a method of Racke originally designed for oblivious routing, we provide an algorithm to construct a HCD and prove that an (O(log n);O(1))-HCD can always be found for any n-node input graph. Further, our algorithm does not rely on a pre-specified number of communities or depth of decomposition. Since the algorithm is of exponential complexity, we also describe a practical efficient, yet heuristic, implementation and perform an experimental validation on synthetic and real-world networks. We experiment first with synthetic networks with well-defined "intended" decompositions, on which we verify the quality of the decompositions produced by our method. Armed with the confidence these positive results provide, we use our implementation to compute the hierarchical community structure of more complex, real-world networks.
Latency-aware elastic scaling for distributed data stream processing systems

Elastic scaling allows a data stream processing system to react to a dynamically changing query or event workload by automatically scaling in or out. Thereby, both unpredictable load peaks as well as underload situations can be handled. However, each scaling decision comes with a latency penalty due to the required operator movements. Therefore, in practice an elastic system might be able to improve the system utilization, however it is not able to provide latency guarantees defined by a service level agreement (SLA). In this paper we introduce an elastic scaling system, which optimizes the utilization under certain latency constraints defined by a SLA. Specifically, we present a model, which estimates the latency spike created by a set of operator movements. We use this model to built a latency-aware elastic operator placement algorithm, which minimizes the number of latency violations. We show that our solution is able to reduce the 90th percentile of the end to end latency by up to 30% and reduce the number of latency violations by 50%. The achieved system utilization for our approach is comparable to a scaling strategy, which does not use latency as optimization target.
A social actor understanding of the institutional structures at play in information systems development

Purpose – This paper seeks to provide insights into the process of applying Lamb & Kling's user as social actor model as an institutional lens to analyse data and to generate findings of IS practice.Design/methodology/approach – The paper provides an example from a recently published research project why the social actor model was chosen, and an application of how the model was operationalised in data collection and analysis.Findings – The paper provides two illustrations of theorising the social actor model at different levels: at an elementary level by using instantiation to map the model's constructs to a match in the transcript text; and at a higher level where the model enabled the paper to tie together a number of individual phenomena to construct a broader higher‐level schema.Originality/value – As the social actor model is relatively new, and few guidelines or examples exist, the paper provides rich insights into the difficulties with the application of this model and a solution for coping with th.
How not to be a black-box : evolution and genetic-engineering of high-level behaviours

In spite of many success stories in various domains, Genetic Algorithm and Genetic Programming still suffer from some significant pitfalls. Those evolved programs often lack of some important properties such as robustness, comprehensibility, transparency, modifiability and usability of domain knowledge easily available. We attempt to resolve these problems, at least in evolving high-level behaviours, by adopting a technique of conditions-and-behaviours originally used for minimizing the learning space in reinforcement learning. We experimentally validate the approach on a foraging task.
Combining Syntax and Thematic Fit in a Probabilistic Model of Sentence Processing

We present a model of human sentence processing that extends a standard probabilistic grammar model with a semantic module which computes the thematic t of verbs and arguments in a cognitively plausible way. Our model differs from existing probabilistic accounts (e.g., Jurafsky, 1996) by capturing both syntactic and semantic inuences in human sentence processing. It also overcomes limitations of constraint-based models (Spivey-Knowlton, 1996; Narayanan and Jurafsky, 2002), as its parameters can be acquired automatically from corpus data, and no hand-coding of constraints is required. We evaluate our semantic module against human ratings of thematic t, and also test the complete model’s performance for two wellstudied ambiguities from the sentence processing literature.