Reviewerly/exHarmony huggingface dataset:
A Mongolian-Chinese Neural Machine Translation Model Based on Soft Target Templates and Contextual Knowledge

In recent years, Mongolian-Chinese neural machine translation (MCNMT) technology has made substantial progress. However, the establishment of the Mongolian dataset requires a significant amount of financial and material investment, which has become a major obstacle to the performance of MCNMT. Pre-training and fine-tuning technology have also achieved great success in the field of natural language processing, but how to fully exploit the potential of pre-training language models (PLMs) in MCNMT has become an urgent problem to be solved. Therefore, this paper proposes a novel MCNMT model based on the soft target template and contextual knowledge. Firstly, to learn the grammatical structure of target sentences, a selection-based parsing tree is adopted to generate candidate templates that are used as soft target templates. The template information is merged with the encoder-decoder framework, fully utilizing the templates and source text information to guide the translation process. Secondly, the translation model learns the contextual knowledge of sentences from the BERT pre-training model through the dynamic fusion mechanism and knowledge extraction paradigm, so as to improve the model’s utilization rate of language knowledge. Finally, the translation performance of the proposed model is further improved by integrating contextual knowledge and soft target templates by using a scaling factor. The effectiveness of the modified model is verified by a large number of data experiments, and the calculated BLEU (BiLingual Evaluation Understudy) value is increased by 4.032 points compared with the baseline MCNMT model of Transformers.
Forward Security Under Leakage Resilience, Revisited

As both notions employ the same key-evolution paradigm, Bellare et al. (CANS 2017) study combining forward security with leakage resilience. The idea is for forward security to serve as a hedge in case at some point the full key gets exposed from the leakage. In particular, Bellare et al. combine forward security with continual leakage resilience, dubbed FS+CL. Our first result improves on Bellare et al.’s FS+CL secure PKE scheme by building one from any continuous leakage-resilient binary-tree encryption (BTE) scheme; in contrast, Bellare et al. require extractable witness encryption. Our construction also preserves leakage rate of the underlying BTE scheme and hence, in combination with existing CL-secure BTE, yields the first FS+CL secure encryption scheme with optimal leakage rate from standard assumptions. We next explore combining forward security with other notions of leakage resilience. Indeed, as argued by Dziembowski et al. (CRYPTO 2011), it is desirable to have a deterministic key-update procedure, which FS+CL does not allow for arguably pathological reasons. To address this, we combine forward security with entropy-bounded leakage (FS+EBL). We construct FS+EBL non-interactive key exchange (NIKE) with deterministic key update based on indistinguishability obfuscation ( $$i\mathcal {O}$$ ), and DDH or LWE. To make the public keys constant size, we rely on the Superfluous Padding Assumption (SuPA) of Brzuska and Mittelbach (ePrint 2015) without auxiliary information, making it more plausible. SuPA notwithstanding, the scheme is also the first FS-secure NIKE from $$i\mathcal {O}$$ rather than multilinear maps. We advocate a future research agenda that uses FS+EBL as a hedge for FS+CL, whereby a scheme achieves the latter if key-update randomness is good and the former if not.
Hidden Stream Ciphers and TMTO Attacks on TLS 1.3, DTLS 1.3, QUIC, and Signal

Transport Layer Security (TLS) 1.3 and the Signal protocol are very important and widely used security protocols. We show that the key update function in TLS 1.3 and the symmetric key ratchet in Signal can be modeled as non-additive synchronous stream ciphers. This means that the efficient Time Memory Tradeoff Attacks for stream ciphers can be applied. The implication is that TLS 1.3, QUIC, DTLS 1.3, and Signal offer a lower security level against TMTO attacks than expected from the key sizes. We provide detailed analyses of the key update mechanisms in TLS 1.3 and Signal, illustrate the importance of ephemeral key exchange, and show that the process that DTLS 1.3 and QUIC use to calculate AEAD limits is flawed. We provide many concrete recommendations for the analyzed protocols.
Teaching of STEM Lectures During the COVID-19 Time

The COVID-19 pandemic situation has adversely affected mobility and international cooperation of students and workers throughout Europe. The transfer of knowledge from foreign experts who can point out the issue in another point of view is an integral part of university studies. However, the reaction of international agency CEEPUS have been greatly flexible a it allowed online and blending mobility to several countries. Thus, the aim of this article is to introduce the possibility how to implement online teaching with the use of foreign workers using available tools and means. The presentation shows practical experience within direct online teaching of STEM subjects in university courses. Primarily, the advantage of using GeoGebra software in online teaching of mathematical subjects is pointed out. Thanks to the aspect of GeoGebra visualization, there was no discomfort in transition of full—time teaching to distance teaching, because understanding of the given topic is not affected by changing the form of teaching. As a part of direct online teaching, a presentation of historical mathematical problems were created; with use of GeoGebra software it facilitated the conversion of historical tasks into a more modern form.
How to Enumerate LWE Keys as Narrow as in Kyber/Dilithium

In the Learning with Errors (LWE) problem we are given a matrix $$A\in \mathbb {Z}_q^{N \times N}$$ and a target vector $$\boldsymbol{t}\in \mathbb {Z}_q^N$$ such that there exists small-norm $$\boldsymbol{s}, \boldsymbol{e}\in \mathbb {Z}_q^N$$ satisfying $$A\cdot \boldsymbol{s}= \boldsymbol{t}+ \boldsymbol{e}\bmod q$$ . Modern cryptosystems often sample $$\boldsymbol{s}, \boldsymbol{e}$$ from narrow distributions that take integer values in a small range $$[-\eta , \eta ].$$ Kyber and Dilithium both choose $$\eta =2$$ and $$\eta =3$$ using either a Centered Binomial distribution (Kyber), or a Uniform distribution (Dilithium). In this work, we address the fundamental question how hard the enumeration of LWE secret keys for narrow distributions with $$\eta \le 3$$ is. At Crypto 21, May proposed a representation-based algorithm for enumerating ternary keys, i.e. the case $$\eta = 1$$ , with a fixed number of $$\pm 1$$ entries. In this work, we extend May’s algorithm in several ways. First, we show how to deal with keys sampled from a probability distribution as in many modern systems like Kyber and Dilithium, rather than with keys having a fixed number of entries. Second, we generalize to larger values $$\eta = 2, 3$$ , thereby achieving asymptotic key guess complexities that are not far off from lattice estimates. E.g. for Kyber’s Centered Binomial distribution we achieve heuristic time/memory complexities of $$\mathcal {O}(2^{0.36N})$$ for $$\eta =2$$ , and $$\mathcal {O}(2^{0.37N})$$ for $$\eta =3$$ . For Dilithium’s Uniform distribution we achieve heuristic complexity $$\mathcal {O}(2^{0.38N})$$ for $$\eta =2$$ . Let $$\mathcal {S}$$ be the Shannon entropy of Kyber/Dilithium keys. Then our algorithms runs in time about $$\mathcal{S}^{\frac{1}{6}}$$ , which greatly improves over the standard combinatorial Meet-in-the-Middle attack with complexity $$\mathcal{S}^{\frac{1}{2}}$$ . Our results also compare well to current lattice asymptotics of $$2^{0.29 \beta }$$ , where the lattice parameter $$\beta $$ is roughly of size $$\frac{4}{5} N$$ . Thus, our analysis supports that Kyber secret keys are indeed hard to enumerate. Yet, we find it remarkable that a purely combinatorial key search is almost competitive with highly evolved lattice sieving techniques.
Towards a Framework for Seismic Data

Over the past decade, Knowledge Graphs (KGs) gained significant attention as a powerful method for knowledge representation. Driven by increasing interest, a paradigm shift has occurred, where the technology of KGs has transitioned from the research domain to the industry and public sector, with companies and organizations increasingly representing their data as Linked Open Data, gaining in that way significant traction for this technology. This paper focuses on KGs in the context of environmental challenges. More specifically, this work concerns KGs that contain seismic event data, such as location, timestamp, magnitude, depth, target date, as well as images before and after the event occurrence. Moreover, a Natural Language Processing (NLP) module is integrated to enhance the KG. That module enables users to query for seismic events in a free-text manner, before addressing them with a relevant response through a dedicated Information Retrieval (IR) component. The KG was constructed with data retrieved from the Instituto Nazionale di Geofisica e Vulcanologia, a rich resource that comes with earthquake-related information, such as magnitude, depth, occurrence location, and timestamp. Additionally, public APIs from the Copernicus Open Access Data Hub and ONDA DIAS are leveraged to provide access to sentinel data, such as images of the event location before and after its occurrence.
A Minor Note on Obtaining Simpler iO Constructions via Depleted Obfuscators

This paper puts forth a simple construction for indistinguishability obfuscation ( $$\textsf{iO}$$ ) for general circuits. The scheme is obtained from four main ingredients: (1) selectively indistinguishably-secure functional encryption for general circuits having its encryption procedure in the complexity class $$\textsf{NC}^1$$ ; (2) universal circuits; (3) puncturable pseudorandom functions having evaluation in $$\textsf{NC}^1$$ ; (4) indistinguishably-secure affine-determinant programs, a notion that particularizes $$\textsf{iO}$$ for specific circuit classes and acts as “depleted” obfuscators. The scheme can be used to build $$\textsf{iO}$$ for all polynomial-sized circuits in a simplified way.
Traceable Policy-Based Signatures with Delegation

In PKC 2014, a policy-based signature (PBS) scheme was proposed by Bellare and Fuchsbauer in which a signer can only sign messages conforming to some policy specified by an issuing authority and the produced signatures are verified under the issuer’s public key. PBS construction supports the delegation of signing policy keys with possible restrictions to the original policy. Although the PBS scheme is meant to limit the signing privileges of the scheme’s users, singers could easily abuse their signing rights without being held accountable since PBS does not have a tracing capability, and a signing policy key defines a policy that should be satisfied by the message only. In this work, we build on PBS and propose a traceable policy-based signature scheme (TPBS) where we employ a rerandomizable signature scheme, a digital signature scheme, and a zero-knowledge proof system as its building blocks. TPBS introduces the notion of identity keys that are used with the policy keys for signing. Thus it achieves traceability without compromising the delegatability feature of the PBS scheme. Additionally, TPBS ensures non-frameability under the assumption of a corrupted tracing authority. We define and formally prove the security notions of the generic TPBS scheme. Finally, we propose an instantiation of TPBS utilizing the Pointcheval-Sanders rerandomizable signature scheme, Abe et al.’s structure-preserving signature scheme, and Groth-Sahai NIZK system, and analyze its efficiency.
KnowWhereGraph-Lite: A Perspective of the KnowWhereGraph

KnowWhereGraph (KWG) is a massive, geo-enabled knowledge graph with a rich and expressive schema. KWG comes with many benefits including helping to capture detailed context of the data. However, the full KWG can be commensurately difficult to navigate and visualize for certain use cases, and its size can impact query performance and complexity. In this paper, we introduce a simplified framework for discussing and constructing perspectives of knowledge graphs or ontologies to, in turn, construct simpler versions; describe our exemplar KnowWhereGraph-Lite (KWG-Lite), which is a perspective of the KnowWhereGraph; and introduce an interface for navigating and visualizing entities within KWG-Lite called KnowWherePanel.
Curatorial Challenges of Exhibiting VR IDN’s in Film Festival Formats

This paper aims to present the current curatorial challenges of exhibiting or showcasing seated narrative VR experiences or installation-type free-roaming VR works, including VR IDNs that represent complexity. The observations primarily relate to exhibitions that take place in the framework of film festivals, and are partly based on personal curatorial experiences of the authors.
Accountant’s Perceptions of the Digital Transformation

As a result of emerging technologies, there are many new issues about the future of the accounting profession. Accounting professionals, accounting practices and processes will have to adapt; otherwise, the profession can dissolve in other disciplines or even be extinct. This paper aims to analyse the perception of accountants regarding the use of emerging technologies such as Cloud Computing, Business Intelligence, Artificial Intelligence and Robotics. The work identifies threats and challenges for the accounting profession resulting from these emerging technologies. The study follows a quantitative approach, using a questionnaire applied to 225 Portuguese-certified accountants. Data analysis of the study used a structural equation model with the approach of partial least squares. We conclude that the perceived usefulness of emerging technologies strongly influences the intention to use them. According to the study, the main threats and challenges to the use of emergent technologies are the increasing cybersecurity risk, the implementation costs, the difficulty in adapting to new working conditions with the suppression of work posts and the resistance to change, particularly of older professionals. This study contributes to a better understanding and exploration of the future direction of the accounting profession.
Figure of Speech Detection and Generation as a Service in IDN Authoring Support

IDN authoring is a complex and creative endeavour. This paper provides the basis for a collaborative text-based authoring tool to support the work with figures of speech, such as metaphors, similes and metonymies. Several models have been tested and BERT, as best performing, has been used for creating functions to predict a figure of speech and, using an embedding of the custom database, return figures of speeches that are closest to the input, and generating new figures of speech. The implemented functions were validated using writers active in different fields. All the code, the databases and the models can be found in this GitHub: link to GitHub .
Interpretation as Play: A Cognitive Psychological Model of Inference and Situation Model Construction

Interpretation of narrative can itself be considered a form of play that is psychologically engaging and effortful. Previous work has provided initial considerations of the cognitive psychology of narrative interpretation, particularly of how players derive pleasure from understanding story. This paper elaborates on the specific processes involved in the comprehension of narrative, providing a review of discourse comprehension theories that culminates in a synthesised summary model of inference and situation model construction. The applicability of this psychological model to players of digital games is considered through comparison to participant descriptions of playing Gone Home. Prevalent themes reflect the relevance-determination of inference and demonstrate the activation of long-term memory in recall of expectations from personal experiences, games, and other media. This is briefly considered in application to storytelling in games and IDN. Future work in this area will focus on the operationalisation of this psychological understanding in practice in game design and storytelling.
Bridging the Gap Between the Physical and the Virtual in Tabletop Role Playing Games: Exploring Immersive VR Tabletops

Throughout the history of tabletop role playing games (TTRPG), the image most associated with playing are people sitting around a table with notebooks, pencils, and occasionally map grids and miniatures. The Internet, however, especially in the post COVID-19 era, has enabled new ways of distance play and the emergence of virtual tabletop applications. In this work we explore the potential of immersive VR to support remote TTRPG play through a user study based on an immersive VR virtual tabletop application prototype. Using this virtual tabletop as an incentive, we invited 11 TTRPG players to offer their views and perspectives on the strengths and weaknesses of VR to support gameplay. We focus on key TTRPG characteristics and motivations, including shared imagination, creativity and sociality, to inform the design of immersive VR tabletop applications tailored to the needs of remote TTRPG play.
VR Storytelling to Prime Uncertainty Avoidance

In recent years, there has been a growing interest among researchers in the field of virtual reality (VR) storytelling. There is a lack of studies on using VR storytelling to prime culture-related content. The cultural aspects, particularly the tendency to avoid uncertainty, have yet to be thoroughly examined within VR. Therefore, we developed VR storytelling intending to prime individuals’ uncertainty avoidance values. An experiment was conducted to assess the efficacy of VR storytelling in priming individuals’ uncertainty avoidance values. The participants’ encounter with VR storytelling was assessed through various parameters, including but not limited to their experience of presence and engagement in the virtual environment. The study provides evidence that VR storytelling has the capacity to influence individuals’ cultural values, particularly their inclination to uncertainty avoidance. Furthermore, the feedback provided by the participants revealed that they had positive emotions, a feeling of being present, engagement, and immersion while engaging with such VR storytelling.
Designing Sisters: Creating Audio-Based Narratives to Generate Affective Connections and Material Story Worlds

In this paper, we reflect on the design of an interactive audio-based digital narrative experience called Sisters. This work is designed for a single interactor, constructed as a mobile AR experience using graphic illustrations on a deck of player cards in connection with abstract audio activated by trigger images on the cards. An interactor is asked to cluster series of cards together into different abstract environments, based on sounds associated with each card, meant to represent spaces in an interior/exterior domestic site, a house and its immediate surroundings. The work conveys experiences of 4 family members in a complex abusive household, mediating between scenes of normalcy, love, companionship, and violence. The core focus of the work is to explore fragmented and very personal states of being and memories derived from an outsider’s perspective (the interactor), who co-experiences the complexities of the domestic spaces at a ‘safe’ distance, while also gaining empathy and affective connections to the characters. Connecting the content of the work and its fragmentary and elusive material audio and narrative design to our design model, the New Material/Spectral Morphology Model, we share how it may be used for aesthetic composition. Our model is based on feminist new material perspectives and foundational work from electroacoustic production and audio experimentation. Sisters extends our previous work with sound-based narrative, and we demonstrate how this work affirms our design strategies for novel interactive audio experiences.
A Paraconsistent Artificial Neural Cell of Learning by Contradiction Extraction (PANCLCTX) with Application Examples

Paraconsistent logic (PL) allows decision-making systems to deal with uncertainty and contradictory signals offering an alternative to the classical logic. Using the interpretations and equations of the PL, it is possible to program blocks of codes called Paraconsistent Artificial Neural Cells (PANCs) used to design data processing systems through paraconsistent artificial neural networks (PANnets). Among the several types of paraconsistent cells, one of the most used is the Paraconsistent Artificial Neural Cell of learning (PANCL). In this chapter, we present a new form of the PANCL based on extraction of the contradiction effects between the input and the previous output, called PANCLCTX. The results presented here demonstrate that the proposed cell works effectively integrating asymptotic mode values, with practical applications for industry in signal analysis, estimation and treatment.
ChatGPT as a Narrative Structure Interpreter

Narrative structures define the skeleton of narratives and help at identifying common structures in stories, that then can be used to compare structures, define variations, and understand prototypical [structural] components. However, narrative structures are just one piece of the puzzle, their interpretation is what gives room to the stories seen in transmedia storytelling. In principle, a structure can be interpreted and developed with a myriad of stories, but requires some type of corpus to develop it further. Large language models such as ChatGPT could be employed for this task, if we are able to define a good narrative structure and give the tools to the algorithm to develop them further. For this paper, we use a narrative structure system called TropeTwist, which employs interconnected tropes as narrative structures, defining characters, conflicts, and plot devices in a relational graph, which gives raise to a set of trope micro- and meso-patterns. Using ChatGPT and through the web interface, we communicate all the possible elements to be used from TropeTwist and tasked ChatGPT to interpret them and generate stories. We describe our process and methodology to reach these interpretations, and present some of the generated stories based on a constructed narrative structure. Our results show the possibilities and limitations of using these systems and elaborate on future work to combine large language models for other tasks within narrative interpretation and generation.
Designing a Language Model-Based Authoring Tool Prototype for Interactive Storytelling

This paper proposes our initial authoring tool prototype using a language model to write interactive stories with narrative elements such as plot types and character arcs. The current prototype focuses on the overall design of the prototype and leaves detailed interactivity and multimedia features for further studies. The results of a simple evaluation using ChatGPT for the 72 generated stories show that the users’ input logline and the selection of the narrative elements can affect the output story quality, such as empathy, surprise, and immersion.