Miracle-dz/UniFAR-FTUs huggingface dataset:
MBT: A Memory-Based Part of Speech Tagger-Generator

We introduce a memory-based approach to part of speech tagging. Memory-based learning is a form of supervised learning based on similarity-based reasoning. The part of speech tag of a word in a particular context is extrapolated from the most similar cases held in memory. Supervised learning approaches are useful when a tagged corpus is available as an example of the desired output of the tagger. Based on such a corpus, the tagger-generator automatically builds a tagger which is able to tag new text the same way, diminishing development time for the construction of a tagger considerably. Memory-based tagging shares this advantage with other statistical or machine learning approaches. Additional advantages specific to a memory-based approach include (i) the relatively small tagged corpus size sufficient for training, (ii) incremental learning, (iii) explanation capabilities, (iv) flexible integration of information in case representations, (v) its non-parametric nature, (vi) reasonably good results on unknown words without morphological analysis, and (vii) fast learning and tagging. In this paper we show that a large-scale application of the memory-based approach is feasible: we obtain a tagging accuracy that is on a par with that of known statistical approaches, and with attractive space and time complexity properties when using IGTree, a tree-based formalism for indexing and searching huge case bases. The use of IGTree has as additional advantage that optimal context size for disambiguation is dynamically computed. 1 I n t r o d u c t i o n Part of Speech (POS) tagging is a process in which syntactic categories are assigned to words. It can be seen as a mapping from sentences to strings of tags. Automatic tagging is useful for a number of applications: as a preprocessing stage to parsing, in information retrieval, in text to speech systems, in corpus linguistics, etc. The two factors determining the syntactic category of a word are its lexical probability (e.g. without context, man is more probably a noun than a verb), and its contextual probability (e.g. after a pronoun, man is more probably a verb than a noun, as in they man the boats). Several approaches have been proposed to construct automatic taggers. Most work on statistical methods has used n-gram models or Hidden Markov Model-based taggers (e.g. Church, 1988; DeRose, 1988; Cutting et al. 1992; Merialdo, 1994, etc.). In
A Peer-to-Peer Browsable File Index using a Popularity Based Global Namespace

The distribution of files using decentralized, peer-to-peer (P2P) systems, has significant advantages over centralized approaches. It is however more difficult to settle on the best approach for file sharing. Most file sharing systems are based on query string searches, leading to a relatively simple but inefficient broadcast or to an efficient but relatively complicated index in a structured environment. In this paper we use a browsable peer-to-peer file index consisting of files which serve as directory nodes, interconnecting to form a directory network. We implemented the system based on BitTorrent and Kademlia. The directory network inherits all of the advantages of decentralization and provides browsable, efficient searching. To avoid conflict between users in the P2P system while also imposing no additional restrictions, we allow multiple versions of each directory node to simultaneously exist -- using popularity as the basis for default browsing behavior. Users can freely add files and directory nodes to the network. We show, using a simulation of user behavior and file quality, that the popularity based system consistently leads users to a high quality directory network; above the average quality of user updates. Q
On P vs. NP, Geometric Complexity Theory, and the Flip I: a high level view

Geometric complexity theory (GCT) is an approach to the $P$ vs. $NP$ and related problems through algebraic geometry and representation theory. This article gives a high-level exposition of the basic plan of GCT based on the principle, called the flip, without assuming any background in algebraic geometry or representation theory.
A study of the edge-switching Markov-chain method for the generation of random graphs

We study the problem of generating connected random graphs with no self-loops or multiple edges and that, in addition, have a given degree sequence. The generation method we focus on is the edge-switching Markov-chain method, whose functioning depends on a parameter w related to the method's core operation of an edge switch. We analyze two existing heuristics for adjusting w during the generation of a graph and show that they result in a Markov chain whose stationary distribution is uniform, thus ensuring that generation occurs uniformly at random. We also introduce a novel w-adjusting heuristic which, even though it does not always lead to a Markov chain, is still guaranteed to converge to the uniform distribution under relatively mild conditions. We report on extensive computer experiments comparing the three heuristics' performance at generating random graphs whose node degrees are distributed as power laws.
Statistical Decision-Tree Models for Parsing

Syntactic natural language parsers have shown themselves to be inadequate for processing highly-ambiguous large-vocabulary text, as is evidenced by their poor performance on domains like the Wall Street Journal, and by the movement away from parsing-based approaches to text-processing in general. In this paper, I describe SPATTER, a statistical parser based on decision-tree learning techniques which constructs a complete parse for every sentence and achieves accuracy rates far better than any published result. This work is based on the following premises: (1) grammars are too complex and detailed to develop manually for most interesting domains; (2) parsing models must rely heavily on lexical and contextual information to analyze sentences accurately; and (3) existing n-gram modeling techniques are inadequate for parsing models. In experiments comparing SPATTER with IBM's computer manuals parser, SPATTER significantly outperforms the grammar-based parser. Evaluating SPATTER against the Penn Treebank Wall Street Journal corpus using the PARSEVAL measures, SPATTER achieves 86% precision, 86% recall, and 1.3 crossing brackets per sentence for sentences of 40 words or less, and 91% precision, 90% recall, and 0.5 crossing brackets for sentences between 10 and 20 words in length.
Immersive Virtual Reality for Treatment of Unilateral Spatial Neglect via Eye-Tracking Biofeedback: RCT Protocol and Usability Testing

About one-third of stroke survivors present unilateral spatial neglect (USN) that negatively impacts the rehabilitation outcome. We reported the study protocol and usability results of an eye-tracking (ET) biofeedback immersive virtual reality (iVR) protocol. Healthy controls and stroke patients with and without USN underwent a single session of the three iVR tasks. The system usability scale (SUS), adverse events (AEs), and ET data were collected and analyzed via parametric analysis. Twelve healthy controls (six young adults and six older adults) and seven patients with a diagnosis of single ischemic stroke (four without USN and three with confirmed diagnosis of USN) completed the usability investigation. SUS results showed good acceptability of the system for healthy controls and stroke patients without USN. ET results showed a lower performance for patients with USN concerning healthy controls and stroke patients without USN, in particular in the exploration of the left visual field. The results showed that the proposed iVR-ET biofeedback protocol is a safe and well-tolerated technique in patients with USN. The real-time feedback can induce a performance response supporting its investigation such as a treatment approach.
STUDI PENERAPAN METODE POHON CONTOH (TREE SAMPLING) DALAM PENDUGAAN POTENSI TEGAKAN HUTAN TANAMAN EKALIPTUS

Management and exploitation of forest products required for planning to be prepared for sustainable forest exploitation and also taking into account aspects of Sustainability. The so-called aspects are production capabilities, ecological functions and social functions. In order to achieve these matters, appropriate data are needed to present information on the potential for existing forest stands through forest inventory activities. In the inventory activity found some problems that occur in terms of cost, time, problems and human resources needed. Factors that will affect the data we get. These inhibiting factors can be overcome by creating three groups using methods that have been developed both in data retrieval techniques and data processing.The sampling method as a suitable and effective method in which the sampling method used in the inventory activity is a conventional method using a plot or a circle plot with the area and sampling determined according to the age class of the stand. In addition, using conventional methods is another method that also has accurate and precise information. Method of sampling trees. This study aims to the effectiveness and effectiveness of tree sampling methods and circular plot methods, making it easier to find an efficient and effective location in the work area of ​​PT. Perawang Sukses Perkasa Petapahan District. The research was conducted in the plantation of industrial plant Ekaliptus (Eucalyptus pellita) located at PT. Perawang Sukses Perkasa Industri Petapahan District, Kampar District, Riau Province. This research was conducted for 2 months ie in May - June 2018. Based on the results of research conducted method of circular plot will be more effective and has a higher level of accuracy than other methods with a value of 2.9% sampling error. When viewed from a relative point of view, the relative average yield using the tree sampling method of 8 trees is more easily done in a circular manner with a relative value of 200.28% and accuracy is still accountable.
Communication Complexity and Secure Function Evaluation

We suggest two new methodologies for the design of efficient secure protocols, that differ with respect to their underlying computational models. In one methodology we utilize the communication complexity tree (or branching for f and transform it into a secure protocol. In other words, "any function f that can be computed using communication complexity c can be can be computed securely using communication complexity that is polynomial in c and a security parameter". The second methodology uses the circuit computing f, enhanced with look-up tables as its underlying computational model. It is possible to simulate any RAM machine in this model with polylogarithmic blowup. Hence it is possible to start with a computation of f on a RAM machine and transform it into a secure protocol. We show many applications of these new methodologies resulting in protocols efficient either in communication or in computation. In particular, we exemplify a protocol for the "millionaires problem", where two participants want to compare their values but reveal no other information. Our protocol is more efficient than previously known ones in either communication or computation.
The Capacity of Random Ad hoc Networks under a Realistic Link Layer Model

The problem of determining asymptotic bounds on the capacity of a random ad hoc network is considered. Previous approaches assumed a threshold-based link layer model in which a packet transmission is successful if the SINR at the receiver is greater than a fixed threshold. In reality, the mapping from SINR to packet success probability is continuous. Hence, over each hop, for every finite SINR, there is a non-zero probability of packet loss. With this more realistic link model, it is shown that for a broad class of routing and scheduling schemes, a fixed fraction of hops on each route have a fixed non-zero packet loss probability. In a large network, a packet travels an asymptotically large number of hops from source to destination. Consequently, it is shown that the cumulative effect of per-hop packet loss results in a per-node throughput of only O(1/n) (instead of Theta(1/sqrt{n log{n}})) as shown previously for the threshold-based link model). A scheduling scheme is then proposed to counter this effect. The proposed scheme improves the link SINR by using conservative spatial reuse, and improves the per-node throughput to O(1/(K_n sqrt{n log{n}})), where each cell gets a transmission opportunity at least once every K_n slots, and K_n tends to infinity as n tends to infinity.
Efficient Implementation of a Semantic-based Transfer Approach

This article gives an overview of a new semantic-based transfer approach developed and applied within the Verbmobil Machine Translation project [22]. We present the declarative transfer formalism and discuss its implementation. The results presented in this paper have been integrated successfully in the Verbmobil system.
Sum Rate Maximization using Linear Precoding and Decoding in the Multiuser MIMO Downlink

We propose an algorithm to maximize the instantaneous sum data rate transmitted by a base station in the downlink of a multiuser multiple-input, multiple-output system. The transmitter and the receivers may each be equipped with multiple antennas and each user may receive more than one data stream. We show that maximizing the sum rate is closely linked to minimizing the product of mean squared errors (PMSE). The algorithm employs an uplink/downlink duality to iteratively design transmit-receive linear precoders, decoders, and power allocations that minimize the PMSE for all data streams under a sum power constraint. Numerical simulations illustrate the effectiveness of the algorithm and support the use of the PMSE criterion in maximizing the overall instantaneous data rate.
Bennett's integral for vector quantizers

This paper extends Bennett's (1948) integral from scalar to vector quantizers, giving a simple formula that expresses the rth-power distortion of a many-point vector quantizer in terms of the number of points, point density function, inertial profile, and the distribution of the source. The inertial profile specifies the normalized moment of inertia of quantization cells as a function of location. The extension is formulated in terms of a sequence of quantizers whose point density and inertial profile approach known functions as the number of points increase. Precise conditions are given for the convergence of distortion (suitably normalized) to Bennett's integral. Previous extensions did not include the inertial profile and, consequently, provided only bounds or applied only to quantizers with congruent cells, such as lattice and optimal quantizers. The new version of Bennett's integral provides a framework for the analysis of suboptimal structured vector quantizers. It is shown how the loss in performance of such quantizers, relative to optimal unstructured ones, can be decomposed into point density and cell shape losses. As examples, these losses are computed for product quantizers and used to gain further understanding of the performance of scalar quantizers applied to stationary, memoryless sources and of transform codes applied to Gaussian sources with memory. It is shown that the short-coming of such quantizers is that they must compromise between point density and cell shapes. >
An Explicit Finite Difference Method and a New von Neumann-Type Stability Analysis for Fractional Diffusion Equations

A numerical method for solving the fractional diffusion equation, which could also be easily extended to other fractional partial differential equations, is considered. In this paper we combine the forward time centered space (FTCS) method, well known for the numerical integration of ordinary diffusion equations, with the Grunwald--Letnikov discretization of the Riemann--Liouville derivative to obtain an explicit FTCS scheme for solving the fractional diffusion equation. The stability analysis of this scheme is carried out by means of a powerful and simple new procedure close to the well-known von Neumann method for nonfractional partial differential equations. The analytical stability bounds are in excellent agreement with numerical test. A comparison between exact analytical solutions and numerical predictions is made.
Minimum-weight double-tree shortcutting for Metric TSP: Bounding the approximation ratio

The Metric Traveling Salesman Problem (TSP) is a classical NP-hard optimization problem. The double-tree shortcutting method for Metric TSP yields an exponentially-sized space of TSP tours, each of which approximates the optimal solution within at most a factor of 2. We consider the problem of finding among these tours the one that gives the closest approximation, i.e.\ the \emph{minimum-weight double-tree shortcutting}. Previously, we gave an efficient algorithm for this problem, and carried out its experimental analysis. In this paper, we address the related question of the worst-case approximation ratio for the minimum-weight double-tree shortcutting method. In particular, we give lower bounds on the approximation ratio in some specific metric spaces: the ratio of 2 in the discrete shortest path metric, 1.622 in the planar Euclidean metric, and 1.666 in the planar Minkowski metric. The first of these lower bounds is tight; we conjecture that the other two bounds are also tight, and in particular that the minimum-weight double-tree method provides a 1.622-approximation for planar Euclidean TSP.
Jaime dos Santos Cardoso Classification of Ordinal Data

Predictive learning has traditionally been a standard inductive learning, where different subproblem formulations have been identified. One of the most representative is classification, consisting on the estimation of a mapping from the feature space into a finite class space. Depending on the cardinality of the finite class space we are left with binary or multiclass classification problems. Finally, the presence or absence or a “natural” order among classes will separate nominal from ordinal problems. Although two-class and nominal classification problems have been dissected in the literature, the ordinal sibling has not yet received a lot of attention, even with many learning problems involving classifying examples into classes which have a natural order. Scenarios in which it is natural to rank instances occur in many fields, such as information retrieval, collaborative filtering, econometric modeling and natural sciences. Conventional methods for nominal classes or for regression problems could be employed to solve ordinal data problems; however, the use of techniques designed specifically for ordered classes yields simpler classifiers, making it easier to interpret the factors that are being used to discriminate among classes, and generalises better. Although the ordinal formulation seems conceptually simpler than nominal, some technical difficulties to incorporate in the algorithms this piece of additional information – the order – may explain the widespread use of conventional methods to tackle the ordinal data problem. This dissertation addresses this void by proposing a nonparametric procedure for the classification of ordinal data based on the extension of the original dataset with additional variables, reducing the classification task to the well-known two-class problem. This framework unifies two well-known approaches for the classification of ordinal categorical data, the minimum margin principle and the generic approach by Frank and Hall. It also presents a probabilistic interpretation for the neural network model. A second novel model, the unimodal model, is also introduced and a parametric version is mapped into neural networks. Several case studies are presented to assert the validity of the proposed models.
Automatic Detection of Trends in Dynamical Text: An Evolutionary Approach

This paper presents an evolutionary algorithm for modeling the arrival dates of document streams, which is any time-stamped collection of documents, such as newscasts, e-mails, IRC conversations, scientific journals archives and weblog postings. This algorithm assigns frequencies (number of document arrivals per time unit) to time intervals so that it produces an optimal fit to the data. The optimization is a trade off between accurately fitting the data and avoiding too many frequency changes; this way the analysis is able to find fits which ignore the noise. Classical dynamic programming algorithms are limited by memory and efficiency requirements, which can be a problem when dealing with long streams. This suggests to explore alternative search methods which allow for some degree of uncertainty to achieve tractability. Experiments have shown that the designed evolutionary algorithm is able to reach the same solution quality as those classical dynamic programming algorithms in a shorter time. We have also explored different probabilistic models to optimize the fitting of the date streams, and applied these algorithms to infer whether a new arrival increases or decreases {\em interest} in the topic the document stream is about.
Substructure Discovery Using Minimum Description Length and Background Knowledge

The ability to identify interesting and repetitive substructures is an essential component to discovering knowledge in structural data. We describe a new version of our SUBDUE substructure discovery system based on the minimum description length principle. The SUBDUE system discovers substructures that compress the original data and represent structural concepts in the data. By replacing previously-discovered substructures in the data, multiple passes of SUBDUE produce a hierarchical description of the structural regularities in the data. SUBDUE uses a computationally-bounded inexact graph match that identifies similar, but not identical, instances of a substructure and finds an approximate measure of closeness of two substructures when under computational constraints. In addition to the minimumdescription length principle, other background knowledge can be used by SUBDUE to guide the search towards more appropriate substructures. Experiments in a variety of domains demonstrate SUBDUE's ability to find substructures capable of compressing the original data and to discover structural concepts important to the domain.
Temperature – Potential Evapotranspiration Relationship in Iraq Using Thornthwaite Method

One of the most important outcomes in the water balance equation for any natural area or water body is Evapotranspiration and it is also a crucial component of the hydrologic cycle. The relationship of temperature - evapotranspiration was obtained using the Thornthwaite method in Iraq by dividing the area into (7) sectors according to geographic latitude. Each sector has multi meteorological stations where (32) stations were used with different periods of records. A mathematical relationship was obtained between mean temperature and corrected potential evapotranspiration (PEc) with (97.45) to (99.84) coefficient of determination. Mean temperature has a symmetrical decreasing pattern from south-east towards north-west of Iraq affected by Mediterranean Sea climate conditions, while correcting potential evapotranspiration (PEc) has the opposite direction of increasing values due to a direct relationship with temperature.
A Feedback Reduction Technique for MIMO Broadcast Channels

A multiple antenna broadcast channel with perfect channel state information at the receivers is considered. If each receiver quantizes its channel knowledge to a finite number of bits which are fed back to the transmitter, the large capacity benefits of the downlink channel can be realized. However, the required number of feedback bits per mobile must be scaled with both the number of transmit antennas and the system SNR, and thus can be quite large in even moderately sized systems. It is shown that a small number of antennas can be used at each receiver to improve the quality of the channel estimate provided to the transmitter. As a result, the required feedback rate per mobile can be significantly decreased