Population size predicts lexical diversity, but so does the mean sea level - why it is important to correctly account for the structure of temporal data

In order to demonstrate why it is important to correctly account for the (serial dependent) structure of temporal data, we document an apparently spectacular relationship between population size and lexical diversity: for five out of seven investigated languages, there is a strong relationship between population size and lexical diversity of the primary language in this country. We show that this relationship is the result of a misspecified model that does not consider the temporal aspect of the data by presenting a similar but nonsensical relationship between the global annual mean sea level and lexical diversity. Given the fact that in the recent past, several studies were published that present surprising links between different economic, cultural, political and (socio-)demographical variables on the one hand and cultural or linguistic characteristics on the other hand, but seem to suffer from exactly this problem, we explain the cause of the misspecification and show that it has profound consequences. We demonstrate how simple transformation of the time series can often solve problems of this type and argue that the evaluation of the plausibility of a relationship is important in this context. We hope that our paper will help both researchers and reviewers to understand why it is important to use special models for the analysis of data with a natural temporal ordering

Profiling of OCR'ed Historical Texts Revisited

In the absence of ground truth it is not possible to automatically determine the exact spectrum and occurrences of OCR errors in an OCR'ed text. Yet, for interactive postcorrection of OCR'ed historical printings it is extremely useful to have a statistical profile available that provides an estimate of error classes with associated frequencies, and that points to conjectured errors and suspicious tokens. The method introduced in Reffle (2013) computes such a profile, combining lexica, pattern sets and advanced matching techniques in a specialized Expectation Maximization (EM) procedure. Here we improve this method in three respects: First, the method in Reffle (2013) is not adaptive: user feedback obtained by actual postcorrection steps cannot be used to compute refined profiles. We introduce a variant of the method that is open for adaptivity, taking correction steps of the user into account. This leads to higher precision with respect to recognition of erroneous OCR tokens. Second, during postcorrection often new historical patterns are found. We show that adding new historical patterns to the linguistic background resources leads to a second kind of improvement, enabling even higher precision by telling historical spellings apart from OCR errors. Third, the method in Reffle (2013) does not make any active use of tokens that cannot be interpreted in the underlying channel model. We show that adding these uninterpretable tokens to the set of conjectured errors leads to a significant improvement of the recall for error detection, at the same time improving precision

TCP throughput guarantee in the DiffServ Assured Forwarding service: what about the results?

Since the proposition of Quality of Service architectures by the IETF, the interaction between TCP and the QoS services has been intensively studied. This paper proposes to look forward to the results obtained in terms of TCP throughput guarantee in the DiffServ Assured Forwarding (DiffServ/AF) service and to present an overview of the different proposals to solve the problem. It has been demonstrated that the standardized IETF DiffServ conditioners such as the token bucket color marker and the time sliding window color maker were not good TCP traffic descriptors. Starting with this point, several propositions have been made and most of them presents new marking schemes in order to replace or improve the traditional token bucket color marker. The main problem is that TCP congestion control is not designed to work with the AF service. Indeed, both mechanisms are antagonists. TCP has the property to share in a fair manner the bottleneck bandwidth between flows while DiffServ network provides a level of service controllable and predictable. In this paper, we build a classification of all the propositions made during these last years and compare them. As a result, we will see that these conditioning schemes can be separated in three sets of action level and that the conditioning at the network edge level is the most accepted one. We conclude that the problem is still unsolved and that TCP, conditioned or not conditioned, remains inappropriate to the DiffServ/AF service

Adaptive Latency Insensitive Protocols andElastic Circuits with Early Evaluation: A Comparative Analysis

AbstractLatency Insensitive Protocols (LIP) and Elastic Circuits (EC) solve the same problem of rendering a design tolerant to additional latencies caused by wires or computational elements. They are performance-limited by a firing semantics that enforces coherency through a lazy evaluation rule: Computation is enabled if all inputs to a block are simultaneously available. Adaptive LIP's (ALIP) and EC with early evaluation (ECEE) increase the performance by relaxing the evaluation rule: Computation is enabled as soon as the subset of inputs needed at a given time is available. Their difference in terms of implementation and behavior in selected cases justifies the need for the comparative analysis reported in this paper. Results have been obtained through simple examples, a single representative case-study already used in the context of both LIP's and EC and through extensive simulations over a suite of benchmarks

Adaptive Load Balancing: A Study in Multi-Agent Learning

We study the process of multi-agent reinforcement learning in the context of load balancing in a distributed system, without use of either central coordination or explicit communication. We first define a precise framework in which to study adaptive load balancing, important features of which are its stochastic nature and the purely local information available to individual agents. Given this framework, we show illuminating results on the interplay between basic adaptive behavior parameters and their effect on system efficiency. We then investigate the properties of adaptive load balancing in heterogeneous populations, and address the issue of exploration vs. exploitation in that context. Finally, we show that naive use of communication may not improve, and might even harm system efficiency.Comment: See http://www.jair.org/ for any accompanying file

MinoanER: Schema-Agnostic, Non-Iterative, Massively Parallel Resolution of Web Entities

Entity Resolution (ER) aims to identify different descriptions in various Knowledge Bases (KBs) that refer to the same entity. ER is challenged by the Variety, Volume and Veracity of entity descriptions published in the Web of Data. To address them, we propose the MinoanER framework that simultaneously fulfills full automation, support of highly heterogeneous entities, and massive parallelization of the ER process. MinoanER leverages a token-based similarity of entities to define a new metric that derives the similarity of neighboring entities from the most important relations, as they are indicated only by statistics. A composite blocking method is employed to capture different sources of matching evidence from the content, neighbors, or names of entities. The search space of candidate pairs for comparison is compactly abstracted by a novel disjunctive blocking graph and processed by a non-iterative, massively parallel matching algorithm that consists of four generic, schema-agnostic matching rules that are quite robust with respect to their internal configuration. We demonstrate that the effectiveness of MinoanER is comparable to existing ER tools over real KBs exhibiting low Variety, but it outperforms them significantly when matching KBs with high Variety.Comment: Presented at EDBT 2001

Sympathy Begins with a Smile, Intelligence Begins with a Word: Use of Multimodal Features in Spoken Human-Robot Interaction

Recognition of social signals, from human facial expressions or prosody of speech, is a popular research topic in human-robot interaction studies. There is also a long line of research in the spoken dialogue community that investigates user satisfaction in relation to dialogue characteristics. However, very little research relates a combination of multimodal social signals and language features detected during spoken face-to-face human-robot interaction to the resulting user perception of a robot. In this paper we show how different emotional facial expressions of human users, in combination with prosodic characteristics of human speech and features of human-robot dialogue, correlate with users' impressions of the robot after a conversation. We find that happiness in the user's recognised facial expression strongly correlates with likeability of a robot, while dialogue-related features (such as number of human turns or number of sentences per robot utterance) correlate with perceiving a robot as intelligent. In addition, we show that facial expression, emotional features, and prosody are better predictors of human ratings related to perceived robot likeability and anthropomorphism, while linguistic and non-linguistic features more often predict perceived robot intelligence and interpretability. As such, these characteristics may in future be used as an online reward signal for in-situ Reinforcement Learning based adaptive human-robot dialogue systems.Comment: Robo-NLP workshop at ACL 2017. 9 pages, 5 figures, 6 table

A Flexible Network Approach to Privacy of Blockchain Transactions

For preserving privacy, blockchains can be equipped with dedicated mechanisms to anonymize participants. However, these mechanism often take only the abstraction layer of blockchains into account whereas observations of the underlying network traffic can reveal the originator of a transaction request. Previous solutions either provide topological privacy that can be broken by attackers controlling a large number of nodes, or offer strong and cryptographic privacy but are inefficient up to practical unusability. Further, there is no flexible way to trade privacy against efficiency to adjust to practical needs. We propose a novel approach that combines existing mechanisms to have quantifiable and adjustable cryptographic privacy which is further improved by augmented statistical measures that prevent frequent attacks with lower resources. This approach achieves flexibility for privacy and efficency requirements of different blockchain use cases.Comment: 6 pages, 2018 IEEE 38th International Conference on Distributed Computing Systems (ICDCS