nils-herrmann/clean_scirepeval huggingface dataset:
Automatically clustering similar units for unit selection in speech synthesis

This paper describes a new method for synthesizing speech by concatenating sub-word units from a database of labelled speech. A large unit inventory is created by automatically clustering units of the same phone class based on their phonetic and prosodic context. The appropriate cluster is then selected for a target unit offering a small set of candidate units. An optimal path is found through the candidate units based on their distance from the cluster center and an acoustically based join cost. Details of the method and justification are presented. The results of experiments using two different databases are given, optimising various parameters within the system. Also a comparison with other existing selection based synthesis techniques is given showing the advantages this method has over existing ones. The method is implemented within a full text-to-speech system offering efficient natural sounding speech synthesis.
Optimizing an ANSI C Interpreter with Superoperators

This paper introduces superoperators, an optimization technique for bytecoded interpreters. Superoperators are virtual machine operations automatically synthesized from smaller operations to avoid costly per-operation overheads. Superoperators decrease executable size and can double or triple the speed of interpreted programs. The paper describes a simple and effective heuristic for inferring powerful superoperators from the usage patterns of simple operators.
The paper describes the design and implementation of a hybrid translator/interpreter that employs superoperators. From a specification of the superoperators (either automatically inferred or manually chosen), the system builds an efficient implementation of the virtual machine in assembly language. The system is easily retargetable and currently runs on the MIPS R3000 and the SPARC.
Functional Entity Relationship Model and Update Operations

A data model, called the entity-relationship model, is proposed. This model incorporates some of the important semantic information in the real world. A special diagramatic technique is introduced as a tool for data base design. An example of data base design and description using the model and the diagramatic technique is given. Some implications on data integrity, information retrieval, and data manipulation are discussed.The entity-relationship model can be used as a basis for unification of different views of data: the network model, the relational model, and the entity set model. Semantic ambiguities in these models are analyzed. Possible ways to derive their views of data from the entity-relationship model are presented.
Automatic question generation from text - an aid to independent study

This report describes an experimental computer-based educational system called automatic question generation (AUTOQUEST) for assisting independent study of written text. Studies of reading comprehension have shown that retention of material is enhanced if the student is periodically required to answer questions about what he has read (Anderson & Biddle, 1975; Anderson et al., 1974; Alessi et al., 1974; Anderson et al., 1975a, 1975b). This principle has been employed in computer-managed instruction, but it requires considerable human effort to prepare the questions.
Node, Node-Link, and Node-Link-Group Diagrams: An Evaluation

Effectively showing the relationships between objects in a dataset is one of the main tasks in information visualization. Typically there is a well-defined notion of distance between pairs of objects, and traditional approaches such as principal component analysis or multi-dimensional scaling are used to place the objects as points in 2D space, so that similar objects are close to each other. In another typical setting, the dataset is visualized as a network graph, where related nodes are connected by links. More recently, datasets are also visualized as maps, where in addition to nodes and links, there is an explicit representation of groups and clusters. We consider these three Techniques, characterized by a progressive increase of the amount of encoded information: node diagrams, node-link diagrams and node-link-group diagrams. We assess these three types of diagrams with a controlled experiment that covers nine different tasks falling broadly in three categories: node-based tasks, network-based tasks and group-based tasks. Our findings indicate that adding links, or links and group representations, does not negatively impact performance (time and accuracy) of node-based tasks. Similarly, adding group representations does not negatively impact the performance of network-based tasks. Node-link-group diagrams outperform the others on group-based tasks. These conclusions contradict results in other studies, in similar but subtly different settings. Taken together, however, such results can have significant implications for the design of standard and domain snecific visualizations tools.
Cyberspace Forensics Readiness and Security Awareness Model

The goal of reaching a high level of security in wireless and wired communication networks is continuously proving difficult to achieve. The speed at which both keepers and violators of secure networks are evolving is relatively close. Nowadays, network infrastructures contain a large number of event logs captured by Firewalls and Domain Controllers (DCs). However, these logs are increasingly becoming an obstacle for network administrators in analyzing networks for malicious activities. Forensic investigators mission to detect malicious activities and reconstruct incident scenarios is extremely complex considering the number, as well as the quality of these event logs. This paper presents the building blocks for a model for automated network readiness and awareness. The idea for this model is to utilize the current network security outputs to construct forensically comprehensive evidence. The proposed model covers the three vital phases of the cybercrime management chain, which are: 1) Forensics Readiness, 2) Active Forensics, and 3) Forensics Awareness. Network Forensics; Forensics Readiness; Network Security; Active Forensics; Reactive Forensics; Forensics Awareness and Network Security model
Understanding user situational relevance in ranking web search results

Relevance plays a key role in determining how ranking features are weighed in rank algorithms. In particular, the Search Engine Result Page (SERP) presents documents from the algorithmic relevance perspective. User relevance, however, is situational and describes the searcher's perception of the contextual document which is interpreted at rank time. Therefore, without it the monotonic decreasing click trend portrayed by rank algorithms will not hold for real time searchers. Instead, users select low ranked documents over high ones or reject the ranking, leading to frustration and several re-query tasks. To understand users situational relevance (USR), we formulate a decreasing probability function to measure each user's personalized probabilistic relevance (PPR). Next, we find an aggregated PPR representing the USR across several query categories. Then, we apply maximum probabilities swapping to modify the rank function and re-rank documents. Finally, we use Normalized Discounted Cumulative Gain (NDCG) and click weight metrics for evaluation. Our results show that a clear interpretation and incorporation of user situational relevance improves click satisfaction by 12.86%, and optimizes click through rate up to 42%.
Finite-State Transducers in Language and Speech Processing

Finite-state machines have been used in various domains of natural language processing. We consider here the use of a type of transducers that supports very efficient programs: sequential transducers. We recall classical theorems and give new ones characterizing sequential string-tostring transducers. Transducers that output weights also play an important role in language and speech processing. We give a specific study of string-to-weight transducers, including algorithms for determinizing and minimizing these transducers very efficiently, and characterizations of the transducers admitting determinization and the corresponding algorithms. Some applications of these algorithms in speech recognition are described and illustrated.
Accurate Full Body Scanning from a Single Fixed 3D Camera

3D body modeling has been a long studied topic in computer vision and computer graphics. While several solutions have been proposed using either multiple sensors or a moving sensor, we propose here an approach when the user turns, in a natural motion, in front of a fixed 3D low cost camera. This opens the door to a wide range of applications where scanning is performed at home. Our scanning system can be easily set up and the instructions are straightforward to follow. We propose an articulated, part-based cylindrical representation for the body model, and show that accurate 3D shape can be automatically estimated from 4 key views detected from a depth video sequence. The registration between 4 key views is performed in a top-bottom-top manner which fully considers the kinematic constraints. We validate our approach on a large number of users, and compare accuracy to that of a reference laser scan. We show that even using a simplified model (5 cylinders) an average error of 5mm can be consistently achieved.
An Analysis of Stochastic Game Theory for Multiagent Reinforcement Learning

Learning behaviors in a multiagent environment is crucial for developing and adapting multiagent systems. Reinforcement learning techniques have addressed this problem for a single agent acting in a stationary environment, which is modeled as a Markov decision process (MDP). But, multiagent environments are inherently non-stationary since the other agents are free to change their behavior as they also learn and adapt. Stochastic games, first studied in the game theory community, are a natural extension of MDPs to include multiple agents. In this paper we contribute a comprehensive presentation of the relevant techniques for solving stochastic games from both the game theory community and reinforcement learning communities. We examine the assumptions and limitations of these algorithms, and identify similarities between these algorithms, single agent reinforcement learners, and basic game theory techniques. This research was sponsored by the United States Air Force under Cooperative Agreements No. F30602-97-2-0250 and No. F30602-98-2-0135. The views and conclusions contained in this document are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of the Defense Advanced Research Projects Agency (DARPA), the Air Force, or the US Government.
Evolutionary Function Approximation for Reinforcement Learning

Temporal difference methods are theoretically grounded and empirically effective methods for addressing reinforcement learning problems. In most real-world reinforcement learning tasks, TD methods require a function approximator to represent the value function. However, using function approximators requires manually making crucial representational decisions. This thesis investigates evolutionary function approximation, a novel approach to automatically selecting function approximator representations that enable efficient individual learning. This method evolves individuals that are better able to learn. I present a fully implemented instantiation of evolutionary function approximation which combines NEAT, a neuroevolutionary optimization technique, with Q-learning, a popular TD method. The resulting NEAT+Q algorithm automatically discovers effective representations for neural network function approximators. This thesis also presents on-line evolutionary computation, which improves the on-line performance of evolutionary computation by borrowing selection mechanisms used in TD methods to choose individual actions and using them in evolutionary computation to select policies for evaluation. I evaluate these contributions with extended empirical studies in two domains: 1) the mountain car task, a standard reinforcement learning benchmark on which neural network function approximators have previously performed poorly and 2) server job scheduling, a large probabilistic domain drawn from the field of autonomic computing. The results demonstrate that evolutionary function approximation can significantly improve the performance of TD methods and on-line evolutionary computation can significantly improve evolutionary methods.
Efficient identification of Web communities

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Obstruction-Free Synchronization: Double-Ended Queues as an Example

We introduce obstruction-freedom, a new nonblocking property for shared data structure implementations. This property is strong enough to avoid the problems associated with locks, but it is weaker than previous nonblocking properties—specifically lock-freedom and wait-freedom— allowing greater flexibility in the design of efficient implementations. Obstruction-freedom admits substantially simpler implementations, and we believe that in practice it provides the benefits of wait-free and lock-free implementations. To illustrate the benefits of obstruction-freedom, we present two obstruction-free CAS-based implementations of double-ended queues (deques); the first is implemented on a linear array, the second on a circular array. To our knowledge, all previous nonblocking deque implementations are based on unrealistic assumptions about hardware support for synchronization, have restricted functionality, or have operations that interfere with operations at the opposite end of the deque even when the deque has many elements in it. Our obstruction-free implementations have none of these drawbacks, and thus suggest that it is much easier to design obstruction-free implementations than lock-free and waitfree ones. We also briefly discuss other obstruction-free data structures and operations that we have implemented.
Distributed Mutual Exclusion Algorithms for Intersection Traffic Control

Traffic control at intersections is a key issue and hot research topic in intelligent transportation systems. Existing approaches, including traffic light scheduling and trajectory maneuver, are either inaccurate and inflexible or complicated and costly. More importantly, due to the dynamics of traffic, it is really difficult to obtain the optimal solution in a real-time way. Inspired by the emergence of vehicular ad hoc network, we propose a novel approach to traffic control at intersections. Via vehicle to vehicle or vehicle to infrastructure communications, vehicles can compete for the privilege of passing the intersection, i.e., traffic is controlled via coordination among vehicles. Such an approach is flexible and efficient. To realize the coordination among vehicles, we first model the problem as a new variant of the classic mutual exclusion problem, and then design algorithms to solve new problem. Both centralized and distributed algorithms are. We conduct extensive simulations to evaluate the performance of our proposed algorithms. The results show that, our approach is efficient and outperforms a reference algorithm based on optimal traffic light scheduling. Moreover, our approach does not rely on traffic light or intersection controller facilities, which makes it flexible and applicable to various kinds of intersections.
3D Object Proposals for Accurate Object Class Detection

In the Supplementary Material we first provide a description of the architecture of our object detection network. We then describe some details about our road estimation method. We also introduce the class-agnostic variant of our approach. We provide comprehensive results on object detection and orientation estimation, and recall statistics for both 2D and 3D box proposals. Finally, a visualization of success cases as well as failure modes are presented, followed by a detailed error analysis of our detector.
K-Means Clustering in Wireless Sensor Networks

A wireless sensor network (WSN) consists of spatially distributed autonomous sensors to monitor physical or environmental conditions and to cooperatively pass their data through the network to a Base Station. Clustering is a critical task in Wireless Sensor Networks for energy efficiency and network stability. Clustering through Central Processing Unit in wireless sensor networks is well known and in use for a long time. Presently clustering through distributed methods is being developed for dealing with the issues like network lifetime and energy. In our work, we implemented both centralized and distributed k-means clustering algorithm in network simulator. k-means is a prototype based algorithm that alternates between two major steps, assigning observations to clusters and computing cluster centers until a stopping criterion is satisfied. Simulation results are obtained and compared which show that distributed clustering is efficient than centralized clustering.
A Particle-and-Density Based Evolutionary Clustering Method for Dynamic Networks

Recently, dynamic networks are attracting increasing interest due to their high potential in capturing natural and social phenomena over time. Discovery of evolutionary communities in dynamic networks has become a critical task. The previous evolutionary clustering methods usually adopt the temporal smoothness framework, which has a desirable feature of controlling the balance between temporal noise and true concept drift of communities. They, however, have some major drawbacks: (1) assuming only a fixed number of communities over time; and (2) not allowing arbitrary start/stop of community over time. The forming of new communities and dissolving of existing communities are very common phenomena in real dynamic networks. In this paper, we propose a new particle-and-density based evolutionary clustering method that efficiently discovers a variable number of communities of arbitrary forming and dissolving. We first model a dynamic network as a collection of lots of particles called nano-communities, and a community as a densely connected subset of particles, called a quasi l-clique-by-clique (shortly, l-KK ). Each particle contains a small amount of information about the evolution of data or patterns, and the quasi l-KK s inherent in a given dynamic network provide us with guidance on how to find a variable number of communities of arbitrary forming and dissolving. We propose a density-based clustering method that efficiently finds temporally smoothed local clusters of high quality by using a cost embedding technique and optimal modularity. We also propose a mapping method based on information theory that makes sequences of smoothed local clusters as close as possible to data-inherent quasi l-KKs. The result of the mapping method allows us to easily identify the stage of each community among the three stages: evolving, forming, and dissolving. Experimental studies, by using various data sets, demonstrate that our method improves the clustering accuracy, and at the same time, the time performance by an order of magnitude compared with the current state-of-the art method. Permission to copy without fee all or part of this material is granted provided that the copies are not made or distributed for direct commercial advantage, the VLDB copyright notice and the title of the publication and its date appear, and notice is given that copying is by permission of the Very Large Data Base Endowment. To copy otherwise, or to republish, to post on servers or to redistribute to lists, requires a fee and/or special permission from the publisher, ACM. VLDB ‘09,August 24-28, 2009, Lyon, France Copyright 2009 VLDB Endowment, ACM 000-0-00000-000-0/00/00.
Wasserstein Generative Adversarial Network

Recent advances in deep generative models give us new perspective on modeling highdimensional, nonlinear data distributions. Especially the GAN training can successfully produce sharp, realistic images. However, GAN sidesteps the use of traditional maximum likelihood learning and instead adopts an two-player game approach. This new training behaves very differently compared to ML learning. There are still many remaining problem of GAN training. In this thesis, we gives a comprehensive review of recently published methods or analysis on GAN training, especially the Wasserstein GAN and FlowGAN model. We also discuss the limitation of the later model and use this as the motivation to propose a novel generator architecture using mixture models. Furthermore, we also modify the discriminator architecture using similar ideas to allow ’personalized’ guidance. We refer the generator mixture model as Mixflow and mixture of discriminators as ’personalized GAN’ (PGAN). In experiment chapter, we demonstrate their performance advantages using toy examples compared to single flow model. In the end, we test their performance on MNIST dataset and the Mixflow model not only achieves the best log likelihood but also produce reasonable images compared to state-of-art DCGAN generation.
Internet Denial of Service Attacks and Defense Mechanisms

Availability is one of the three main components of computer security, along with confidentiality and integrity. Denial of service (DoS) is a threat that potentially violates the availability of a resource in a system. Existing survey of DoS attack and defense mechanisms are relatively outdated, and do not reflect the significant developments in this area in recent years. In this article, we present an in-depth study of the denial of service problem in the Internet, and provide a comprehensive survey of attacks and their countermeasures. We investigate various DoS attack mechanisms, derive a more practical taxonomy of attack mechanisms, and summarize the challenges in DoS defense. We critically review the state of the art in DoS defense, analyze the strengths and weaknesses of different proposals, and conclude a comprehensive taxonomy of various defense mechanisms.