Reviewerly/exHarmony huggingface dataset:
Reflective Hybrid Intelligence for Meaningful Human Control in Decision-Support Systems

With the growing capabilities and pervasiveness of AI systems, societies must collectively choose between reduced human autonomy, endangered democracies and limited human rights, and AI that is aligned to human and social values, nurturing collaboration, resilience, knowledge and ethical behaviour. In this chapter, we introduce the notion of self-reflective AI systems for meaningful human control over AI systems. Focusing on decision support systems, we propose a framework that integrates knowledge from psychology and philosophy with formal reasoning methods and machine learning approaches to create AI systems responsive to human values and social norms. We also propose a possible research approach to design and develop self-reflective capability in AI systems. Finally, we argue that self-reflective AI systems can lead to self-reflective hybrid systems (human + AI), thus increasing meaningful human control and empowering human moral reasoning by providing comprehensible information and insights on possible human moral blind spots.
SparqLog: A System for Efficient Evaluation of SPARQL 1.1 Queries via Datalog [Experiment, Analysis and Benchmark]

Over the past decade, Knowledge Graphs have received enormous interest both from industry and from academia. Research in this area has been driven, above all, by the Database (DB) community and the Semantic Web (SW) community. However, there still remains a certain divide between approaches coming from these two communities. For instance, while languages such as SQL or Datalog are widely used in the DB area, a different set of languages such as SPARQL and OWL is used in the SW area. Interoperability between such technologies is still a challenge. The goal of this work is to present a uniform and consistent framework meeting important requirements from both, the SW and DB field.
AICT: An Adaptive Image Compression Transformer

Motivated by the efficiency investigation of the Tranformer-based transform coding framework, namely SwinT-ChARM, we propose to enhance the latter, as first, with a more straightforward yet effective Tranformer-based channel-wise auto-regressive prior model, resulting in an absolute image compression transformer (ICT). Current methods that still rely on ConvNet-based entropy coding are limited in long-range modeling dependencies due to their local connectivity and an increasing number of architectural biases and priors. On the contrary, the proposed ICT can capture both global and local contexts from the latent representations and better parameterize the distribution of the quantized latents. Further, we leverage a learnable scaling module with a sandwich ConvNeXt-based pre/post-processor to accurately extract more compact latent representation while reconstructing higher-quality images. Extensive experimental results on benchmark datasets showed that the proposed adaptive image compression transformer (AICT) framework significantly improves the trade-off between coding efficiency and decoder complexity over the versatile video coding (VVC) reference encoder (VTM-18.0) and the neural codec SwinT-ChARM.
52 Weeks Later: Attitudes Towards COVID-19 Apps for Different Purposes Over Time

The COVID-19 pandemic has prompted countries around the world to introduce smartphone apps to support disease control efforts. Their purposes range from digital contact tracing to quarantine enforcement to vaccination passports, and their effectiveness often depends on widespread adoption. While previous work has identified factors that promote or hinder adoption, it has typically examined data collected at a single point in time or focused exclusively on digital contact tracing apps. In this work, we conduct the first representative study that examines changes in people's attitudes towards COVID-19-related smartphone apps for five different purposes over the first 1.5 years of the pandemic. In three survey rounds conducted between Summer 2020 and Summer 2021 in the United States and Germany, with approximately 1,000 participants per round and country, we investigate people's willingness to use such apps, their perceived utility, and people's attitudes towards them in different stages of the pandemic. Our results indicate that privacy is a consistent concern for participants, even in a public health crisis, and the collection of identity-related data significantly decreases acceptance of COVID-19 apps. Trust in authorities is essential to increase confidence in government-backed apps and foster citizens' willingness to contribute to crisis management. There is a need for continuous communication with app users to emphasize the benefits of health crisis apps both for individuals and society, thus counteracting decreasing willingness to use them and perceived usefulness as the pandemic evolves.
Exposing the Fake: Effective Diffusion-Generated Images Detection

Image synthesis has seen significant advancements with the advent of diffusion-based generative models like Denoising Diffusion Probabilistic Models (DDPM) and text-to-image diffusion models. Despite their efficacy, there is a dearth of research dedicated to detecting diffusion-generated images, which could pose potential security and privacy risks. This paper addresses this gap by proposing a novel detection method called Stepwise Error for Diffusion-generated Image Detection (SeDID). Comprising statistical-based $\text{SeDID}_{\text{Stat}}$ and neural network-based $\text{SeDID}_{\text{NNs}}$, SeDID exploits the unique attributes of diffusion models, namely deterministic reverse and deterministic denoising computation errors. Our evaluations demonstrate SeDID's superior performance over existing methods when applied to diffusion models. Thus, our work makes a pivotal contribution to distinguishing diffusion model-generated images, marking a significant step in the domain of artificial intelligence security.
Model Reduction for Quantum Systems: Discrete-time Quantum Walks and Open Markov Dynamics

A general approach to obtain reduced models for a wide class of discrete-time quantum systems is proposed. The obtained models not only reproduce exactly the output of a given quantum model, but are also guaranteed to satisfy physical constraints, namely complete positivity and preservation of total probability. A fundamental framework for exact model reduction of quantum systems is constructed leveraging on algebraic methods, as well as novel results on quantum conditional expectations in finite-dimensions. The proposed reduction algorithm is illustrated and tested on prototypical examples, including the quantum walk realizing Grover's algorithm.
Audio-Driven Talking Face Generation: A Review

Given a face image and a speech audio, talking face generation refers to synthesizing a face video speaking the given speech.It has wide applications in movie dubbing, teleconference, virtual assistant, etc.This paper gives an overview of research progress on talking face generation in recent years.The author first reviews traditional talking face generation methods.Then, deep learning talking face generation methods, including talking face synthesis for a specific identity and talking face synthesis for an arbitrary identity, are summarized.The author then surveys recent detail-aware talking face generation methods, including noise based approaches, eye conversion based approaches, and facial anatomy based approaches.Next, the author surveys the talking head generation methods, such as video/image driven talking head generation, pose information-driven talking head generation, and audio-driven talking head generation.Finally, some future directions for talking face generation are highlighted.
The Bivariate Mixture Space: A Compact Spectral Representation of Bivariate Signals

The Fourier Transform (FT) is a widely used analysis tool.However, FT alone is not suited for the analysis of bivariate signals (e.g., stereophonic recordings), as it is not sensitive to the relationship between channels.Different works addressing this problem can be found in the literature; the Bivariate Mixture Space (BMS) is introduced here as an alternative representation to the existing techniques.BMS is still based on the FT and can be thought of as an extension of it, such that the relationship between two signals is considered as additional information in the frequency domain.Despite being simpler than other techniques aimed at representing bivariate signals, this representation is shown to have some desirable characteristics that are absent in traditional representations, which lead to novel ways to perform linear and non-linear decomposition, feature extraction, and data visualization.As a demonstrative application, an Independent Component Analysis algorithm is derived from the BMS, who shows promising results with respect to existing implementations in terms of performance and robustness.
QUESTIONING ANIMALS’ STATUS AS OBJECTS OF PROPERTY RIGHTS IN CROATIAN AND COMPARATIVE PROPERTY LAW

The proper coexistence of humans and other members of the living world is one of the essential preconditions for forming the rule of law. Through the influence of the biocentric concept, under the auspices of bioethical and animal-ethical thinking, the legal regulation of humans and animals develops within the framework of animal law as a relatively new branch of law. One of the central topics within the mentioned legal branch is the discussion of animals as objects of property (proprietary) rights and, potentially, as holders of certain legal rights. The above represents a significant challenge to property law and the theoretical foundations of civil law, given that the Croatian Ownership and Other Proprietary Rights Act does not explicitly mention the status of an animal, but the status of an animal as a thing derives from the Croatian property law doctrine. The paper analytically, historically and comparatively examines the current legal status of animals as objects of property rights. Historical insight refers to the development of the paradigmatic position of animals as things established in Roman private law. Analytical elaboration of current property law statutory provisions and reflection of property law doctrine are placed in the comparative legal context of the civil law provisions of those countries that have recognized animals as non-things or as sentient beings.
Differential Education–Based Methodology for Improving General Competence of Future Specialists

The methods of forming the general professional competence of future specialists based on differentiated education are shown in this scientific research work, as are the methods of using a differential approach in the formation of general professional competence of students, the model of forming the general professional competence of students in the process of differential education has been developed and scientifically based, and the criteria, indicators, and levels of students’ general professional competence have been developed and scientifically based.
Spatiotemporal Self-Attention-Based Network Traffic Prediction in IIoT

The sixth-generation (6G) mobile communications are considered as a future network and very closed to the Industrial Internet of Things (IIoT) due to its low latency and high throughput. Massive nodes supported by 6G make up the complexity of the network. Moreover, the heterogeneous traffic brings difficulties to the network management. Long-term network traffic matrix (TM) prediction is a crucial technology for realizing network edge intelligence and dealing with the above issues. However, predicting long-term network traffic in heterogeneous IIoT is challenging. Due to the powerful feature extraction capability over long sequences, self-attention is widely applied in language inference tasks. Motivated by these observations, we propose a self-attention traffic matrix prediction (SATMP) model for long-term network TM prediction in IIoT scenarios. SATMP consists of three components: (a) a spatial–temporal encoding for obtaining the spatial–temporal features of network TM; (b) a learnable positional encoding for providing positional correlation to the traffic sequence; and (c) a self-attention module for capturing long-term dependence. These components work together to enhance long-term prediction performance in complex networks effectively. Extensive experiments on three publicly available datasets demonstrate that SATMP is feasible and accurate in IIoT long-term network TM prediction.
Metamorphosed marl as a candidate for a previous gabbro: examples from the Iraqi Zagros Collisional Belt, Mawat Ophiolite Complex

57 ABSTRACT In the Iraqi Northern Iraq, the Mawat-Chwarta area is well known, for its Ophiolite Complex and igneous rocks. Tens of studies investigated its petrology, geochemistry and tectonics under the name of Mawat Ophiolite Complex (MOC). According to these studies, MOC is a large part of the oceanic crust which was thrusted hundreds of kilometers as allochthonous sheets and obducted on the passive margin of the Arabian plate during the Late Cretaceous. In the present study, conjugate signals of stratigraphy, structure, rock texture, topography, type of weathering, remote sensing data, boundary condition, fossils hosting, and literature on marl metamorphism, indicated the sedimentary origin of the MOC. These signals confirm the autochthonous nature of MOC and all of its rocks have resulted from the crystallization of two types of sedimentary rocks. The claimed gabbro of the complex (here called Mawat gabbro) is a regional metamorphism of the marl of the Shiranish Formation (hemipelagite of Late Campanian). The second rocks are the claimed peridotite, volcanic rocks and others that resulted from the metamorphism of greywackes and mafic and felsic volcaniclastic arenite of the Walash Formation (Paleocene). The outcrops of the metamorphosed sediments of the latter formation are partially covering the former formation. These results are supported by detailed geologic maps, cross-sections, tectonic models, thin sections and GPS data. These results do not aid the presence of ophiolite and igneous rocks in the Mawat area and introduce significant changes to the geology of the whole Zagros collisional belt.
Learning based Software Defect Prediction

Software Engineering is a Comprehensive domain since software system have become larger and complex than ever.Such software characteristics make it very complex to develop fault free software.Therefore, identifying defects automatically and fixing them is challenging task.Improper modelling, lack of requirement specifications, wrong coding, poor configuration management may cause defects which leads to failure of software system.Such defects must be detected and fixed at early stage of software development to minimize cost.Machine learning algorithms widely used in the software defect prediction also achieves good results in predicting software defects using deep learning techniques.In this paper, we are providing comparative study of various algorithms like convolutional neural networks, multi-layer Perceptrons in identifying defects in software.Moreover, the experiments conducted on NASA datasets.
Language Interpretation Using Machine Learning

Wouldn't it be cool for your device to translate English language into the language you speak in real life and vice versa?Understanding multiple languages can be a challenging task for humans.In a country like India, where there are over 22 recognized languages, it can be nearly impossible for an individual to learn and comprehend all of them.Consequently, communicating effectively in such a diverse linguistic environment can pose significant obstacles, forcing the need of human interpreters.An interpreter is a platform which translates what is being said in one language into another language without changing its original meaning.Google Translate is one such existing platform but it has drawbacks like not ideal for confidential documents, cannot provide a perfectly accurate translation and requires internet connection to translate.Our goal is to address the limitations mentioned earlier, and to accomplish this objective, we have come up with a project idea that involves real-time translation of spoken languages.The project aims to achieve this by using audio as an input.The proposed system will take real-time English audio as an input, translate it into Telugu and then will produce audio output in Telugu and vice versa.As English is the third most spoken native language in the world after Chinese and Spanish and also the most widely used, we chose to convert the text from English to Telugu and vice versa.Our intention is to utilize a Sequence-to-Sequence model as it is proficient in performing language translations.This model works by transforming a sequence of words from one language into a sequence of different words in another language.It uses neural network for sequence learning.
An Efficient Ring Polynomial Multiplication Accelerator for Homomorphic Encryption

Fully homomorphic encryption has become a key technique for solving the conflict between cloud services and privacy preservation. The most time-consuming step in homomorphic schemes is ring polynomial multiplication (RPM). Number theory transform (NTT) and Karatsuba algorithms are efficient to accelerate RPM, yet they are limited by the modulus operations and degrees of the polynomial. The systolic array is adopted for RPM processing recently. However, a modular reduction operation is required as post-processing which increases the overall delay. This paper has proposed a cyclic systolic array architecture without a dedicated reduction unit by re-routing the output of the systolic array for reusing, resulting in a 50% clock cycles saving of processing time. The corresponding FPGA implementation has a reduction of 72.9% and 33.8% when n=256 and n=1024 for equivalent area time product (eATP), respectively, therefore achieving an improved trade-off between performance and resource consumption.
Analysis of Types and Incidents of Neighborhood Business Crimes in the United States of America

Unfortunately, traditional crimes business victims in the United States of America do not have immediate legislative protection to prevent future occurrences. For example, assault weapons (AR-15 &amp; AK-47), Forgery, Theft and Weapon Abuses remains in the business neighborhood of American cities, despite hundreds of assault gun victims in the societies. These business victims often have more moments and memories and have a sad experience than State and Federal Legislature pouring “Thoughts and Prayers” as condolences to the business victims and owners(Entrepreneurs). These lack of Legislatives empathy will never stop the thefts, forgery and even killings in business neighborhood. Therefore, business crimes and incidents needs constant promotion of awareness that my result to network of solutions to the business crimes vocabularies; actions and criminal justice administrative remedies together with the existing laws. The information presented in this article with the suggested model was obtained from personal observations, interviewing entrepreneurs and business owners of business crimes victims. What they felt, for the purpose of formulating important strategies to reduce business crimes in American business neighborhood form the basis of this analysis. This process my successfully balance criminal justice professional actions with existing pending legislation and the aggressively in prosecuting business crime offender (White, &amp; Hertz, 2018). Therefore, the full analysis of the study, is to identify, analyzed and contrast the types of business crimes among within selected American business neighborhood (Malls and Shopping Centers), located in central Alabama. An analysis will statistically utilize the value of .05 level of significance if applicable for each of the issues with the business crime and incidents. Finally, the information and data analysis found that, relationships may exist between business crimes, incidents, business distress and bankruptcy. Contrary to neighborhood free from business crimes offenders and Shopping Malls securities infrastructures.
Progression of Decomposed Local-Effect Action Theories

&lt;p&gt;In many tasks related to reasoning about consequences of a logical theory, it is desirable to decompose the theory into a number of weakly related or independent components. However, a theory may represent knowledge that is subject to change, as a result of executing actions that have effects on some of the initial properties mentioned in the theory. Having once computed a decomposition of a theory, it is advantageous to know whether a decomposition has to be computed again in the newly changed theory (obtained from taking into account changes resulting from execution of an action). In this article, we address this problem in the scope of the situation calculus, where a change of an initial theory is related to the notion of progression. Progression provides a form of forward reasoning; it relies on forgetting values of those properties, which are subject to change, and computing new values for them. We consider decomposability and inseparability, two component properties known from the literature, and contribute by studying the conditions (1) when these properties are preserved and (2) when they are lost wrt progression and the related operation of forgetting. To show the latter, we demonstrate the boundaries using a number of negative examples. To show the former, we identify cases when these properties are preserved under forgetting and progression of initial theories in local-effect basic action theories of the situation calculus. Our article contributes to bridging two different communities in knowledge representation, namely, research on modularity and research on reasoning about actions. &lt;/p&gt;
Goal-Based Software Budgeting Methodology

In the southern area of Itapúa and surrounding areas, there is an accelerated growth of MSMEs, which, being affected by formal electronic information and taxation systems, in addition to the growing volume of information that they must handle, are a niche market.ideal for custom software development where development teams typically consist of one or two people.The main characteristics of these developments is that they have well-defined objectives, and are restrictive in terms of time and budget, so a methodology that covers budgeting at the same time as development is necessary to avoid misunderstandings between clients and developers, as well such as budget losses, underestimation of work, or lack of development and implementation time.In this work I propose a goal-oriented methodology, which focuses on an initial budget that is as exact as possible and accompanies each phase with specific products, that allow the validation of the development results and the quality control of the resulting software throughout the process.
From Thin Concurrent Games to Generalized Species of Structures

Two families of denotational models have emerged from the semantic analysis of linear logic: dynamic models, typically presented as game semantics, and static models, typically based on a category of relations. In this paper we introduce a formal bridge between two-dimensional dynamic and static models: we connect the bicategory of thin concurrent games and strategies, based on event structures, to the bicategory of generalized species of structures, based on distributors.In the first part of the paper, we construct an oplax functor from (the linear bicategory of) thin concurrent games to distributors. This explains how to view a strategy as a distributor, and highlights two fundamental differences: the composition mechanism, and the representation of resource symmetries.In the second part of the paper, we adapt established methods from game semantics (visible strategies, payoff structure) to enforce a tighter connection between the two models. We obtain a cartesian closed pseudofunctor, which we exploit to shed new light on recent results in the bicategorical theory of the λ-calculus.