Reviewerly/exHarmony huggingface dataset:
Utterances without Force

In this paper the author attempts to reconcile two claims recently defended by Mitchell Green. The first is that illocutionary force is part of speaker meaning (Green 2018). The second is that illocutionary force is a product of cultural evolution (Green 2017). Consistent with the second claim, the author argues that some utterances – particularly those produced by infants and great apes – are produced with communicative intent, but without illocutionary force. These utterances lack the normative properties constitutive of force because their utterers have no grasp of the norms that operate on developed speech. If there can be utterances produced with communicative intent that lack force, we must consider how exactly force is a part of speaker meaning. In response the author argues that force is an inessential and acquired part of speaker meaning. As a result we need a conception of communicative intent more basic than illocutionary intent. He spells this out in terms of a ‘perlocutionary’ intention.
On the Mutual Information of Two Boolean Functions, with Application to Privacy

We investigate the behavior of the mutual information between two Boolean functions of correlated binary strings. The covariance of these functions is found to be a crucial parameter in the aforementioned mutual information. We then apply this result in the analysis of a specific privacy problem where a user observes a random binary string. Under particular conditions, we characterize the optimal strategy for communicating the outcomes of a function of said string while preventing to leak any information about a different function.
How might we increase system trustworthiness?

Summarizing some of the changes that seem increasingly necessary to address known system and network deficiencies and anticipate currently unknown vulnerabilities.
Autocorrelation functions: a useful tool for both state and detector characterisation

Abstract The calculation of autocorrelation functions represents a routinely used tool to characterise quantum states of light. In this paper, we evaluate the g (2) function for detected photons in the case of mesoscopic multi-mode twin-beam states in order to fully investigate their statistical properties starting from measurable quantities. Moreover, we show that the second-order autocorrelation function is also useful to estimate the spurious effects affecting the employed Silicon-photomultiplier detectors.
(Near) Optimal Adaptivity Gaps for Stochastic Multi-Value Probing.

Consider a kidney-exchange application where we want to find a max-matching in a random graph. To find whether an edge $e$ exists, we need to perform an expensive test, in which case the edge $e$ appears independently with a \emph{known} probability $p_e$. Given a budget on the total cost of the tests, our goal is to find a testing strategy that maximizes the expected maximum matching size. The above application is an example of the stochastic probing problem. In general the optimal stochastic probing strategy is difficult to find because it is \emph{adaptive}---decides on the next edge to probe based on the outcomes of the probed edges. An alternate approach is to show the \emph{adaptivity gap} is small, i.e., the best \emph{non-adaptive} strategy always has a value close to the best adaptive strategy. This allows us to focus on designing non-adaptive strategies that are much simpler. Previous works, however, have focused on Bernoulli random variables that can only capture whether an edge appears or not. In this work we introduce a multi-value stochastic probing problem, which can also model situations where the weight of an edge has a probability distribution over multiple values. Our main technical contribution is to obtain (near) optimal bounds for the (worst-case) adaptivity gaps for multi-value stochastic probing over prefix-closed constraints. For a monotone submodular function, we show the adaptivity gap is at most $2$ and provide a matching lower bound. For a weighted rank function of a $k$-extendible system (a generalization of intersection of $k$ matroids), we show the adaptivity gap is between $O(k\log k)$ and $k$. None of these results were known even in the Bernoulli case where both our upper and lower bounds also apply, thereby resolving an open question of Gupta et al.
Characteristic formulae for liveness properties of non-terminating CakeML programs

There are useful programs that do not terminate, and yet standard Hoare logics are not able to prove liveness properties about non-terminating programs. This paper shows how a Hoare-like programming logic framework (characteristic formulae) can be extended to enable reasoning about the I/O behaviour of programs that do not terminate. The approach is inspired by transfinite induction rather than coinduction, and does not require non-terminating loops to be productive. This work has been developed in the HOL4 theorem prover and has been integrated into the ecosystem of proof tools surrounding the CakeML programming language.
Coarse-to-fine segmentation for indoor scenes with progressive supervision

Three-dimensional indoor scene segmentation is highly difficult due to the natural hierarchical structures and complicated contextual relationships in the scenes. In this paper, a 3D scene segmentation method that uses a stacked network is proposed for utilizing the context and hierarchy in 3D scenes. The method consists of two parts: a stacked network and progressive supervision. The stacked network consists of multiple base segmentation networks, and each network's output is concatenated to the raw input as another network's input to provide a prior context. Progressive supervision includes a group of coarse-to-fine segmentation labels that are generated based on the spatial relationships among objects in the scene, and it forces the network to learn the hierarchy. The experimental results from a regular dataset and a complex dataset demonstrate that our progressive supervision is effective and that our method outperforms existing methods in complex scenes.
Linked stage graph

Archives today are publishing their cultural heritage data on the Web for exploration. However, for archive novices the traditional archival structures often are not intuitive and difficult to understand, and thus challenges data access and consumption. To tackle this problem, Linked Stage Graph was developed, a knowledge graph (KG) on the foundation of historical data about the Stuttgart State Theater. The data was made available by the Baden-Wuerttemberg State Archives for the Coding da Vinci hackathon. This demo paper contributes the KG,a SPARQL endpoint, named entity extraction and linking to existing authoritative KGs as well as a dedicated user interface for exploration.
The use of nondialogic trolling to disrupt online communication

Trolling, a uniquely common antisocial behavior in online communities, has traditionally been conceptualized as a phenomenon based in group dialogue. Yet it is possible for trolling to occur without any actual dialogue between the troll and the intended targets. In this paper, several methods of trolling are presented, each of which is performed through engagement with user-generated media artifacts rather than through back-and-forth conversation among users. These are broadly grouped into four categories based on the type of media engagement that is exploited: content creation, content curation, content evaluation, and content refinement. This manuscript therefore fills a significant gap in the literature on this topic and helps to resolve a limitation in existing explications of the construct by showing its applicability to a wider range of behaviors than previously acknowledged.
Word Embeddings for IoT Based on Device Activity Footprints

With the expansion of IoT ecosystem, there is an explosion of the number of devices and sensors and the data generated by these devices. However, the tools available to analyze such data are limited. Word embeddings, widely used in the natural language processing (NLP) domain, provides a way to get similar words to the current word. In this paper, we extend the theory of word embeddings to the area of IoT devices, proposing a method to generate the word embeddings for IoT devices and sensors in a smart home based on their activity. We model IoT devices as vectors using a concept like Word2Vec and App2Vec, where the time between the device firings is also taken into account. These computed word embeddings can be used for a variety of use cases, such as to find similar devices in an IoT device store, or as a signature of each type of IoT device. We show results of a feasibility study on the CASAS dataset and a private real-world dataset of IoT device activity logs, using our method to identify the patterns in embeddings of various types of IoT devices in a household. We get a probability of more than 0.65 for similar types of devices clustering together, independent of session gap value and embedding vector size for the CASAS dataset. We also get a prob-ability of 0.4 on the private dataset, independent of session gap value and embedding vector size.
On the Time Complexity of Simple Cartesian Genetic Programming

Since its introduction, Cartesian Genetic Programming has been mostly analyzed on an experimental level with boolean function problems. Consequently, there is still little theoretical understanding of Cartesian Genetic Programming. In this paper, we present a first time complexity analysis of Cartesian Genetic Programming. We introduce and analyze a simple mathematical problem and a simple logical boolean problem called SUM and AND. The results of our analysis show that simple CGP is able to solve SUM efficiently in time Θ(nlogn). However, our analysis of the AND problem shows that simple CGP is not able to solve AND efficiently.
Formal Grammatical and Ontological Modeling of Corpus Data on Tibetan Compounds

This article provides a consistent formal grammatical and ontological description of the model of the Tibetan compounds system, developed and used for automatic syntactic and semantic analysis of Tibetan texts, on the material of a hand-verified corpus. This model covers all types of Tibetan compounds, which were previously introduced by other authors, and introduces a number of new classes of compounds, taking into account their derivation, structure and semantics. The article describes the tools used for ontological modeling of Tibetan compounds; special attention is paid to the problem of modeling the semantics of verbs and verbal compounds. Nominal and verbal compounds are considered separately, it is noted that the importance of verbal compounds for the Tibetan language system is not less than that of nominal compounds. The statistical data on the absolute frequency distribution of the use of compounds of different types in the current version of the corpus annotation and on the amounts of ontology concepts associated with each class of compounds are given.
Safeguarding NMA Enhanced Galileo OS Signals from Distance-Decreasing Attacks

Increased use of global satellite navigation systems (GNSS), for applications such as autonomous vehicles, intelligent transportation systems and drones, heightens security concerns. Civil GNSS signals are vulnerable to notably spoofing and replay attacks. To counter such attacks, cryptographic methods are developed: Navigation Message Authentication (NMA) is one such scheme, about to be deployed for Galileo E1 Open Service (OS); it allows receivers to verify the signal origin and protects navigation message integrity. However, NMA signals cannot fully thwart replay attacks, which do not require forging navigation messages. Classic replay attacks, e.g, meaconing, retransmit previously recorded signals without any modification, thus highly limiting the capacity of the adversary. Distance-decreasing (DD) attacks are a strong type of replay attack, allowing fine-grained individual pseudorange manipulation in real time. Moreover, DD attacks counterbalance processing and transmission delays induced by adversary, by virtue of shifting earlier in time the perceived (relayed) signal arrival; thus shortening the pseudorange measurements. In this paper, we first analyze how DD attacks can harm the Galileo E1 OSNMA service assuming the adversary has no prior information on the navigation message. Moreover,we propose a DD attack detection method based on a Goodness of Fit test on the prompt correlator outputs of the victim. The results show that the method can detect the DD attacks even when the receiver has locked to the DD signals.
THE EFFECT OF AURASMA AUGMENTED REALITY (AR) TO ENHANCE YOUNG LEARNERS’ VOCABULARY MASTERY

The aim of this study is to analyze the difference of young learners’ vocabulary mastery through Aurasma Augmented Reality (AR). The research was conducted in SDN 1 Jayagiri Lembang and the sample of this study was gained from two classes of sixth grade. One class was treated using AR, while another class was treated without AR in teaching vocabulary. The data were gathered from pre-test and post-test. The result of this study revealed that young learners could improve their vocabulary mastery through augmented reality (AR). It can be inferred that there is a significant difference from students’ pre-test and post-test result. Furthermore, the data from observation displayed a positive attitude during the students’ learning through Aurasma AR. Most of students participated actively in the classroom, they showed high curiosity, and they enjoyed and felt enthusiastic in learning vocabulary.
Mobile Robot Path Planning Based on Improved Particle Swarm optimization

According to the characteristics of particle swarm optimization(PSO), this paper studies on utilizing PSO algorithm to solve the path planning problem of mobile robots in polar coordinate system by polar angle. In order to solve the problem of particles falling into local extreme, which comes from the decline of the diversity of particle population in the later stage of searching, a mutation operation method was proposed. It enables particles to perform mutation operation while retaining most of the previous searching experience. So as to increase the diversity of population and make particles escape from local extreme. For the problem of the path points searched by PSO have many redundant path points, a de-redundant algorithm was proposed to remove them and make the path better. By environment modeling, improved algorithm and other methods are used for path planning. The comparison of simulation analysis shows that the improved PSO algorithm has more effective iterations, the planned path length is shorter, and the running time is not increased, which verifies the effectiveness of the method.
A forest of trees with principal direction specified oblique split on random subspace

No matter whether they are univariate or multivariate decision forests, most of previous decision forests determine their partition hyperplanes at split nodes by exhaustive search from candidates or by random generation, which makes some dent in either efficiency or accuracy. In this paper, we propose a new oblique/multivariate decision forest, a forest of trees with principal direction specified oblique split on random subspace (FPDS), where each split of trees is uniquely deterministic once the random feature subspace is determined, the largest principal direction of Principal Component Analysis (PCA) on the sample data at the corresponding split node and the median value of all the current sample points' projections on the largest principal direction directly specified as the normal direction and the cut-point of the partition hyperplane. This method avoids either tediously searching for the optimal split or casually randomly generating the split. The heuristic method to obtain the hyperplanes guarantees accuracy of trees, and the random feature subspace selection adequately ensures the diversity among individual trees in the forest. In addition, each tree of the FPDS uses the whole training set instead of the sampling subset. Therefore, the only randomness factor in the FPDS derives from the random feature subspace selection, which to some extent enhances the robustness. It proves that the proposed forest FPDS is an alternative classifier which can match or even outperform the existing ensemble classifiers or other classifiers.
Secure Electronic Voting Using a Hybrid Cryptosystem and Steganography

Conventional voting techniques have been employed over the years in most elections. However, each of these techniques has attendant short comings. The existing conventional voting systems have been subjected to gross abuse and irregularities. Electronic voting (e-voting) which is emerging as an alternative to these conventional voting systems, though highly promising, is not also totally free of flaws; information security issues bordering on privacy, integrity and verifiability of the electronic ballots casted are still significant in most implementations of e-voting systems. In this paper, we developed a security scheme that was based on a hybrid Rivest-Sharma-Adleman (RSA) algorithm and Advance Encryption Standard (AES) algorithm with Least Significant Bit (LSB) replacement algorithm for securing ballot information in an e-voting system. An e-voting system with multiple voting channels which includes poll site voting, mobile voting and remote internet voting was developed in the contextual Nigerian scenario. The electronic ballots casted via the system during experimental usage were encrypted using the hybrid RSA-AES algorithm to circumvent the problems of privacy, integrity and verifiability associated with many e-voting systems. The encrypted ballots were then hidden using LSB replacement algorithm to improve their integrity. The image quality of the cover image used and stego image obtained from the security scheme developed was quantitatively assessed using Peak Signal to Noise Ratio (PSNR), Mean Square Error (MSE) and Correlation. Furthermore, a performance evaluation based on users' perception of the developed e-voting system showed that the system satisfied most of the generic security requirements for electronic voting.
Matrices in <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si1.svg"><mml:mi mathvariant="script">A</mml:mi><mml:mo stretchy="false">(</mml:mo><mml:mi>R</mml:mi><mml:mo>,</mml:mo><mml:mi>S</mml:mi><mml:mo stretchy="false">)</mml:mo></mml:math> with minimum t-term ranks

Let R and S be two sequences of nonnegative integers in nonincreasing order which have the same sum, and let A(R,S) be the class of all (0,1)-matrices which have row sums given by R and column sums given by S. For a positive integer t, the t-term rank of a (0,1)-matrix A is defined as the maximum number of 1's in A with at most one 1 in each column and at most t 1's in each row. In this paper, we address conditions for the existence of a matrix in A(R,S) that realizes all the minimum t-term ranks, for t≥1.
Deciding Context Unification

In first-order term unification, variables represent well-formed terms over a given signature, and we are to solve equations built using function symbols from the signature and such variables; this problem is well-known to be decidable (in linear time). In second-order term unification, the variables take arguments (i.e., other terms) and a substitution uses those arguments an arbitrary number of times; for instance, an equation f ( X ( c ), X ( c )) = X ( f ( c , c )) has a solution X = •, where • is a special symbol denoting the place in which the argument is substituted. Under this substitution, both sides evaluate to f ( c , c ). There are other solutions, for instance X = f (•,•), which evaluates both sides to f ( f ( c , c ), f ( c , c )); in general, a solution that evaluates both sides to full binary tree of arbitrary height is easy to construct. Second-order unification is in general undecidable. Context unification is a natural problem in between first- and second-order unification—we deal with equations over terms, the variables take arguments, but we restrict the set of solutions: The argument is used exactly once. Formally, contexts are terms with exactly one occurrence of the special symbol • and in context unification, we are given an equation over terms with variables representing contexts and ask about the satisfiability of this equation. For instance, when the aforementioned equation f ( X ( c ), X ( c )) = X ( f ( c , c )) is treated as a context unification problem, then it has exactly one solution: X = •. Other substitutions that are solutions of it as an instance of the second-unification problem, say X = f (•, •), are not valid, as • is used more than once. Context unification also generalizes satisfiability of word equations, which is decidable (in PSPACE). The decidability status of context unification remained unknown for almost two decades. In this article, we show that context unification is in PSPACE (in EXPTIME , when tree regular constraints are also allowed). Those results are obtained by extending the recently developed recompression technique, which was previously defined for strings and used to obtain a new PSPACE algorithm for satisfiability of word equations. In this article, the technique is generalized to trees, and the corresponding algorithm is generalized from word equations to context unification. The idea of recompression is to apply simple compression rules (replacing pairs of neighboring function symbols) to the solution of the context equation; to this end, we appropriately modify the equation (without the knowledge of the actual solution) so compressing the solution can be simulated by compressing parts of the equation. It is shown that if the compression operations are appropriately chosen, then the size of the instance is polynomial during the whole algorithm, thus giving a PSPACE-upper bound.
The Scrum Culture: Introducing Agile Methods in Organizations <b>The Scrum Culture: Introducing</b> <b>Agile Methods in Organizations</b> , Maximini Dominik, Cham, Switzerland, Springer Nature, 2018, 323 pp., $69.99 (Hardcover), ISBN: 978-3-319-73841-3

Software is an important element in our lives. From mobile apps to embedded systems, software enables many organizations to function. The traditional software development methodology is called the ...
Using a KG-Copy Network for Non-goal Oriented Dialogues

Non-goal oriented, generative dialogue systems lack the ability to generate answers with grounded facts. A knowledge graph can be considered an abstraction of the real world consisting of well-grounded facts. This paper addresses the problem of generating well-grounded responses by integrating knowledge graphs into the dialogue system’s response generation process, in an end-to-end manner. A dataset for non-goal oriented dialogues is proposed in this paper in the domain of soccer, conversing on different clubs and national teams along with a knowledge graph for each of these teams. A novel neural network architecture is also proposed as a baseline on this dataset, which can integrate knowledge graphs into the response generation process, producing well articulated, knowledge grounded responses. Empirical evidence suggests that the proposed model performs better than other state-of-the-art models for knowledge graph integrated dialogue systems.