Reviewerly/exHarmony huggingface dataset:
Interactive Narrative: A Novel Application of Artificial Intelligence for Computer Games

Game Artificial Intelligence (Game AI) is a sub-discipline of Artificial Intelligence (AI) and Machine Learning (ML) that explores the ways in which AI and ML can augment player experiences in computer games. Storytelling is an integral part of many modern computer games; within games stories create context, motivate the player, and move the action forward. Interactive Narrative is the use of AI to create and manage stories within games, creating the perception that the player is a character in a dynamically unfolding and responsive story. This paper introduces Game AI and focuses on the open research problems of Interactive Narrative.
Effects of Age and Rule Familiarity on the Production of Modeled Language Constructions

Most interesting is the fact that the oldest children showed greater increments in relevant constructions in the new rule (NR) than in the English rule condition. The verbal stimuli were 95 nouns which are commonly known by children of the youngest age used. Of particular interest in this study was the influence of rule familiarity and age upon increments in the subjects' production of sentences which precisely fitted the modeled rule. The performance of the subjects exposed to unfamiliar constructions supports this conclusion. Instead of abstracting a new rule from the model's productions, the subjects in these conditions apparently reordered the unfamiliar language constructions to make them correspond to language rules with which they were familiar. An inspection of the repetition errors in the NR condition indicated that most were reordered forms of the modeled NR constructions.
Take-Away Games

Several authors have considered take-away games where the players alternately remove a positive number of counters from a single pile, the player removing the last counter being the winner. On his initial move, the first player may remove any number of counters, so long as he leaves at least one. On each subsequent move, a player may remove at most f(n) counters, where n is the number of counters removed by his opponent on the preceding move. We prove various results (improving all previously known results) about the sequence of losing positions when f is a linear function.
Hierarchic Superposition Revisited

Many applications of automated deduction require reasoning in first-order logic modulo background theories, in particular some form of integer arithmetic. A major unsolved research challenge is to design theorem provers that are “reasonably complete” even in the presence of free function symbols ranging into a background theory sort. The hierarchic superposition calculus of Bachmair, Ganzinger, and Waldmann already supports such symbols, but, as we demonstrate, not optimally. This paper aims to rectify the situation by introducing a novel form of clause abstraction, a core component in the hierarchic superposition calculus for transforming clauses into a form needed for internal operation. We argue for the benefits of the resulting calculus and provide two new completeness results: one for the fragment where all background-sorted terms are ground and another one for a special case of linear (integer or rational) arithmetic as a background theory.
Securing Aggregate Queries for DNA Databases

This paper addresses the problem of sharing person-specific genomic sequences without violating the privacy of their data subjects to support large-scale biomedical research projects. The proposed method builds on the framework proposed by Kantarcioglu et al. [1] but extends the results in a number of ways. One improvement is that our scheme is deterministic, with zero probability of a wrong answer (as opposed to a low probability). We also provide a new operating point in the space-time tradeoff, by offering a scheme that is twice as fast as theirs but uses twice the storage space. This point is motivated by the fact that storage is cheaper than computation in current cloud computing pricing plans. Moreover, our encoding of the data makes it possible for us to handle a richer set of queries than exact matching between the query and each sequence of the database, including: (i) counting the number of matches between the query symbols and a sequence; (ii) logical OR matches where a query symbol is allowed to match a subset of the alphabet thereby making it possible to handle (as a special case) a “not equal to” requirement for a query symbol (e.g., “not a G”); (iii) support for the extended alphabet of nucleotide base codes that encompasses ambiguities in DNA sequences (this happens on the DNA sequence side instead of the query side); (iv) queries that specify the number of occurrences of each kind of symbol in the specified sequence positions (e.g., two `A' and four `C' and one `G' and three `T', occurring in any order in the query-specified sequence positions); (v) a threshold query whose answer is `yes' if the number of matches exceeds a query-specified threshold (e.g., “7 or more matches out of the 15 query-specified positions”). (vi) For all query types, we can hide the answers from the decrypting server, so that only the client learns the answer. (vii) In all cases, the client deterministically learns only the query's answer, except for query type (v) where we quantify the (very small) statistical leakage to the client of the actual count.
Maximum weight spectrum codes

In the recent work [9], a combinatorial problem concerning linear codes over a finite field $\mathbb{F}_q$ was introduced. In that work the authors studied the weight set of an $[n,k]_q$ linear code, that is the set of non-zero distinct Hamming weights, showing that its cardinality is bounded above by $\frac{q^k-1}{q-1}$. They showed that this bound was sharp in the case $ q = 2 $, and in the case $ k = 2 $. They conjectured that the bound is sharp for every prime power $ q $ and every positive integer $ k $. In this work we quickly establish the truth of this conjecture. We provide two proofs, each employing different construction techniques. The first relies on the geometric view of linear codes as systems of projective points. The second approach is purely algebraic. We establish some lower bounds on the length of codes that satisfy the conjecture, and the length of the new codes constructed here are discussed.
Software Engineering Antipatterns in Start-Ups

Software start-up failures are often explained with poor business model, market issues, insufficient funding, or simply a bad product idea. However, inadequacies in software product engineering are relatively little explored and could be a significant contributing factor to high start-up failure rate. In this paper we present analysis of 88 start-up experience reports. The analysis is presented in a form of three anti-patterns illustrating common symptoms, actual causes, and potential countermeasures of engineering inadequacies. The three anti-patterns are: product uncertainty comprising of issues in requirements engineering, poor product quality comprising of inadequacies in product quality, and team breakup comprising of team issues. The anti-patterns show that challenges and failure scenarios that appear to be business or market-related can actually originate from inadequacies in product engineering.
Trojan Quantum Walks

We investigate the transport properties and entanglement between spin and position of one-dimensional quantum walks starting from a qubit over position states following a delta-like (local state) and Gaussian (delocalized state) distributions. We find out that if the initial state is delocalized enough and a NOT gate reflects this state backwards, then the interference pattern extinguishes the position dispersion without preventing the propagation of the state. This effect allows the creation of a Trojan wave packet, a non-spreading and non-stationary double-peak quantum state.
Pragmatism should Not be a Substitute for Statistical Literacy, a Commentary on Albers, Kiers, and Van Ravenzwaaij (2018)

Based on the observation that frequentist confidence intervals and Bayesian credible intervals sometimes happen to have the same numerical boundaries (under very specific conditions), Albers et al. (2018) proposed to adopt the heuristic according to which they can usually be treated as equivalent. We argue that this heuristic can be misleading by showing that it does not generalise well to more complex (realistic) situations and models. Instead of pragmatism, we advocate for the use of parsimony in deciding which statistics to report. In a word, we recommend that a researcher interested in the Bayesian interpretation simply reports credible intervals.
Wavelength assignment in quantum access networks with hybrid wireless-fiber links

We propose a low-complexity near-optimal wavelength allocation technique for quantum key distribution access networks that rely on wavelength division multiple access. Such networks would allow users to send quantum and classical signals simultaneously on the same optical fiber infrastructure. Users can be connected to the access network via optical wireless or wired links. We account for the background noise present in the environment, as well as the Raman noise generated by classical channels, and calculate the secret key generation rate for quantum channels in the finite-key setting. This allows us to examine the feasibility of such systems in realistic scenarios when the secret key exchange needs to be achieved in a limited time scale. Our numerical results show that, by proper choice of system parameters for this noisy system, it is possible to exchange a secret key in tens of seconds. Moreover, our proposed algorithm can enhance the key rate of quantum channels, especially in high noise and/or high loss regimes of operation.
Empirical study on lexical sentiment in passwords from Chinese websites

Passwords are especially ubiquitous in authentication systems. Although a lot of research has concentrated on the analysis of passwords, statistical characteristics are limited to explicit properties such as string length, the use of numeric characters in passwords. We explore the implicit properties of lexical sentiment in passwords by utilizing Natural Language Processing technology. Firstly, we construct several dictionaries, such as frequently used words, General Inquirer polarities, extended Ekman sentiment words. Then, an algorithm to split passwords into meaningful units is designed. Finally, statistical characteristics in lexical sentiment are discovered from three large-scale password sets leaked from the Internet websites. The results show that the occurrence probability of sentiment words is higher than that of several known patterns, such as [country], [number][female name]. With consideration of sentiment polarity, we find that people tend to use positive words in passwords. The percentage of positive sentiment is greater than that of negative words. Further research reveals that the joy type of sentiment is more popular in passwords than other kinds of sentiments, such as surprise and sadness. The discoveries suggest that the lexical sentiment, especially, positive and joy type can be utilized as a component in password patterns to measure password strength.
Changeset-Based Topic Modeling of Software Repositories

The standard approach to applying text retrieval models to code repositories is to train models on documents representing program elements. However, code changes lead to model obsolescence and to the need to retrain the model from the latest snapshot. To address this, we previously introduced an approach that trains a model on documents representing changesets from a repository and demonstrated its feasibility for feature location. In this paper, we expand our work by investigating: a second task (developer identification), the effects of including different changeset parts in the model, the repository characteristics that affect the accuracy of our approach, and the effects of the time invariance assumption on evaluation results. Our results demonstrate that our approach is as accurate as the standard approach for projects with most changes localized to a subset of the code, but less accurate when changes are highly distributed throughout the code. Moreover, our results demonstrate that context and messages are key to the accuracy of changeset-based models and that the time invariance assumption has a statistically significant effect on evaluation results, providing overly-optimistic results. Our findings indicate that our approach is a suitable alternative to the standard approach, providing comparable accuracy while eliminating retraining costs.
Supercomputers to improve the performance in higher education: A review of the literature

The use of Supercomputers is currently very widespread, constituting an essential component in many fields of science. The interest in the use of high performance computation is increasing in a wider and more diverse population of higher education students, mainly senior undergraduates and postgraduates, because the use of these infrastructures allows learners to improve their skills and the results of their training. For this reason, the demand of courses related to supercomputing increases continuously. In this paper we propose, through a wide review of primary studies, several questions that have been considered as a way of knowing the most widely-used contents in Supercomputing training. We have focused on the factors considered for improving training in Supercomputing, in order to improve the results of researchers in higher education organizations, to identify the limitations of Supercomputing training, and to provide solutions for these limitations. During the search procedure for answering research questions, 1911 studies were considered in the first selection. Through the definition of inclusion and exclusion codes in the results of searching databases, 136 published articles were studied. Finally, using quality criteria, 34 studies were identified as relevant in answering the research questions. Several factors were described, such as the way in which courses related to Supercomputing are organized, the adaptations that are currently being applied in curricula related to the students of these techniques, the use of problem-solving training and the qualification of teachers, among the most relevant ones, as well as several limitations of this type of training and the identification of solutions for these limitations. Data was collected by searching keywords related to Supercomputing training and education in the most important databases used in Computational Science, finding empirical evidence to support the positive effect of High Performance Computers (HPC) on educators and researchers. The implications of this study are: first, it provides a summary of the most relevant factors in improving training, as well as the factors that improve the results through the use of a Supercomputer; and second, it provides the analysis of the limitations found for a better performance of learners and the solutions for these limitations.
Upper bound on certifiable randomness from a quantum black-box device

Quantum theory allows for randomness generation in a device-independent setting, where no detailed description of the experimental device is required. Here we derive a general upper bound on the amount of randomness that can be generated in such a setting. Our bound applies to any black-box scenario, thus covering a wide range of scenarios from partially characterised to completely uncharacterised devices. Specifically, we prove that the number of random bits that can be generated is limited by the number of different input states that enter the measurement device. We show explicitly that our bound is tight in the simplest case. More generally, our work indicates that the prospects of generating a large amount of randomness by using high-dimensional (or even continuous variable) systems will be extremely challenging in practice.
An Overview of the Shared Task on Machine Translation in Indian Languages (MTIL) – 2017

Abstract In recent years, the multilingual content over the internet has grown exponentially together with the evolution of the internet. The usage of multilingual content is excluded from the regional language users because of the language barrier. So, machine translation between languages is the only possible solution to make these contents available for regional language users. Machine translation is the process of translating a text from one language to another. The machine translation system has been investigated well already in English and other European languages. However, it is still a nascent stage for Indian languages. This paper presents an overview of the Machine Translation in Indian Languages shared task conducted on September 7–8, 2017, at Amrita Vishwa Vidyapeetham, Coimbatore, India. This machine translation shared task in Indian languages is mainly focused on the development of English-Tamil, English-Hindi, English-Malayalam and English-Punjabi language pairs. This shared task aims at the following objectives: (a) to examine the state-of-the-art machine translation systems when translating from English to Indian languages; (b) to investigate the challenges faced in translating between English to Indian languages; (c) to create an open-source parallel corpus for Indian languages, which is lacking. Evaluating machine translation output is another challenging task especially for Indian languages. In this shared task, we have evaluated the participant’s outputs with the help of human annotators. As far as we know, this is the first shared task which depends completely on the human evaluation.
Locally Consistent Parsing for Text Indexing in Small Space

Previous chapter Next chapter Full AccessProceedings Proceedings of the 2020 ACM-SIAM Symposium on Discrete Algorithms (SODA)Locally Consistent Parsing for Text Indexing in Small SpaceOr Birenzwige, Shay Golan, and Ely PoratOr Birenzwige, Shay Golan, and Ely Poratpp.607 - 626Chapter DOI:https://doi.org/10.1137/1.9781611975994.37PDFBibTexSections ToolsAdd to favoritesExport CitationTrack CitationsEmail SectionsAboutAbstract We consider two closely related problems of text indexing in a sub-linear working space. The first problem is the Sparse Suffix Tree (SST) construction, where a text S is given in read-only memory, along with a set of suffixes B, and the goal is to construct the compressed trie of all these suffixes ordered lexicographically, using only (|B|) words of space. The second problem is the Longest Common Extension (LCE) problem, where again a text S of length n is given in read-only memory with some parameter 1 ≤ τ n, and the goal is to construct a data structure that uses words of space and can compute for any pair of suffixes their longest common prefix length. We show how to use ideas based on the Locally Consistent Parsing technique, that were introduced by Sahinalp and Vishkin [44], in some nontrivial ways in order to improve the known results for the above problems. We introduce new Las-Vegas and deterministic algorithms for both problems. For the randomized algorithms, we introduce the first Las-Vegas SST construction algorithm that takes (n) time. This is an improvement over the last result of Gawrychowski and Kociumaka [22] who obtained (n) time for Monte Carlo algorithm, and time with hight probability for Las-Vegas algorithm. In addition, we introduce a randomized Las-Vegas construction for a data structure that uses words of space, can be constructed in linear time with high probability and answers LCE queries in (τ) time. For the deterministic algorithms, we introduce an SST construction algorithm that takes time (for |B| = Ω(log n)). This is the first almost linear time, (n · polylog n), deterministic SST construction algorithm, where all previous algorithms take at least time. For the LCE problem, we introduce a data structure that uses words of space and answers LCE queries in time, with (n log τ) construction time (for ). This data structure improves both query time and construction time upon the results of Tanimura et al. [47]. Previous chapter Next chapter RelatedDetails Published:2020eISBN:978-1-61197-599-4 https://doi.org/10.1137/1.9781611975994Book Series Name:ProceedingsBook Code:PRDA20Book Pages:xxii + 3011
Ontology-Based Query Answering for Probabilistic Temporal Data

We investigate ontology-based query answering for data that are both temporal and probabilistic, which might occur in contexts such as stream reasoning or situation recognition with uncertain data. We present a framework that allows to represent temporal probabilistic data, and introduce a query language with which complex temporal and probabilistic patterns can be described. Specifically, this language combines conjunctive queries with operators from linear time logic as well as probability operators. We analyse the complexities of evaluating queries in this language in various settings. While in some cases, combining the temporal and the probabilistic dimension in such a way comes at the cost of increased complexity, we also determine cases for which this increase can be avoided.
Deep Neural Networks Constrained by Decision Rules

Deep neural networks achieve high predictive accuracy by learning latent representations of complex data. However, the reasoning behind their decisions is difficult for humans to understand. On the other hand, rule-based approaches are able to justify the decisions by showing the decision rules leading to them, but they have relatively low accuracy. To improve the interpretability of neural networks, several techniques provide post-hoc explanations of decisions made by neural networks, but they cannot guarantee that the decisions are always explained in a simple form like decision rules because their explanations are generated after the decisions are made by neural networks.In this paper, to balance the accuracy of neural networks and the interpretability of decision rules, we propose a hybrid technique called rule-constrained networks, namely, neural networks that make decisions by selecting decision rules from a given ruleset. Because the networks are forced to make decisions based on decision rules, it is guaranteed that every decision is supported by a decision rule. Furthermore, we propose a technique to jointly optimize the neural network and the ruleset from which the network select rules. The log likelihood of correct classifications is maximized under a model with hyper parameters about the ruleset size and the prior probabilities of rules being selected. This feature makes it possible to limit the ruleset size or prioritize human-made rules over automatically acquired rules for promoting the interpretability of the output. Experiments on datasets of time-series and sentiment classification showed rule-constrained networks achieved accuracy as high as that achieved by original neural networks and significantly higher than that achieved by existing rule-based models, while presenting decision rules supporting the decisions.