Reviewerly/exHarmony huggingface dataset:
Generic construction for tightly-secure signatures from discrete log

Tightly secure signature plays a significant role in the research of cryptography and has been studied extensively in the literature. In this paper, we present a generic construction for tightly-secure signatures from the discrete log (DL) assumption in the existential-unforgeability against key only attacks (EUF-KOA) security model, where the adversary is allowed to obtain only the public key, but not any sample signature. Moreover, the generic construction can also be extended into the multi-user setting with corruptions (MU-C) model. Roughly speaking, given any signature scheme, we can efficiently convert it into a signature scheme that features tight security under the DL assumption in the MU-EUF-KOA-C security model with random oracles. Our transformation shows it is easy to construct a DL-equivalent signature in the EUF-KOA security model, although many known DL-based signatures are not equivalent to DL. If the given signature scheme is key-re-randomizable, the transformed scheme is also key-re-randomizable. Hence, our result provides a supplement to Bader et al.'s work (EUROCRYPT 2016).
Measuring Ethics in AI with AI: A Methodology and Dataset Construction

Recently, sound measures and metrics in Artificial Intelligence have become a focus of research and development in academia, government, and industry. Efforts towards measuring different phenomena have gained traction in the AI community, as illustrated by several influential field reports and policy documents. These metrics are designed to help decision-makers inform themselves about the fast-moving and impacting influences of key advances in Artificial Intelligence in general and Machine Learning in particular. In this paper, we propose to use such newfound capabilities of AI technologies to augment our AI measuring capabilities. We do so by training a model to classify publications related to ethical issues and concerns. Our methodology uses an expert, manually curated dataset as the training set and then evaluates an extensive collection of research papers. Finally, we highlight the implications of AI metrics, particularly their contribution towards developing trustful and fair AI-based tools and technologies.
Dialogue summarization with supporting utterance flow modelling and fact regularization

Dialogue summarization aims to generate a summary that indicates the key points of a given dialogue. In this work, we propose an end-to-end neural model for dialogue summarization with two novel modules, namely, the supporting utterance flow modelling module and the fact regularization module. The supporting utterance flow modelling helps to generate a coherent summary by smoothly shifting the focus from the former utterances to the later ones. The fact regularization encourages the generated summary to be factually consistent with the ground-truth summary during model training, which helps to improve the factual correctness of the generated summary in inference time. Furthermore, we also introduce a new benchmark dataset for dialogue summarization. Extensive experiments on both existing and newly-introduced datasets demonstrate the effectiveness of our model.
Subspace Clustering with Block Diagonal Sparse Representation

Structured representation is of remarkable significance in subspace clustering. However, most of the existing subspace clustering algorithms resort to single-structured representation, which may fail to fully capture the essential characteristics of data. To address this issue, a novel multi-structured representation subspace clustering algorithm called block diagonal sparse representation (BDSR) is proposed in this paper. It takes both sparse and block diagonal structured representations into account to obtain the desired affinity matrix. The unified framework is established by integrating the block diagonal prior into the original sparse subspace clustering framework and the resulting optimization problem is iteratively solved by the inexact augmented Lagrange multipliers (IALM). Extensive experiments on both synthetic and real-world datasets well demonstrate the effectiveness and efficiency of the proposed algorithm against the state-of-the-art algorithms.
Asset-Based Extended Reality Model for Distance Learning

Distance learning is a phenomenon that comes to the mind especially when it is difficult or impossible for people to come together for educational purposes. It also makes learning independent from the classroom environment. The COVID-19 epidemic, which covers the world in 2020, has led to the approaches of distance learning through new digital technologies and has revealed the need for more effective and efficient education models with interdisciplinary concepts. The fact that distance learning is independent of the classroom environment directly affects the students’ interest and interaction in the lesson. In face-to-face learning, the interaction between the teacher and the student has not only cognitive but also emotional and physical factors. Lack of these factors in distance learning has negative consequences, especially in learning and experimenting processes. Many different models and methods have been developed for distance learning so far. These models were mostly provided by adapting traditional methods and educational contents to existing technological tools and unfortunately did not bring an effective solution proposal to the specified problem. Extended Reality (XR) applications, which are considered as an exciting technology today, also offer great opportunities for distance learning. Various models have been proposed to use these technologies in distance learning, which provide ideal tools for both spatial and interpersonal interaction with virtual and augmented reality. In this study, the Asset-Based Extended Reality Model was proposed as an alternative to other models and compared by using quantitative data. The model has three pillars; Assets, Gamification, and Immersion.
The Mathematical Aspects of Some Problems from Coding Theory

We briefly review the history and main research directions of the research in department “Mathematical Foundations of Informatics” (MFI) at the Institute of Mathematics and Informatics (IMI) of the Bulgarian Academy of Sciences, founded and headed by Professor Stefan Dodunekov (1945–2012). We describe two major themes which stay in the focus of MFI for many years. We present results and pose some open problems, as well as directions for future research.
On cyclic codes over finite chain rings

Abstract Recent studies involve various approaches to establish a generating set for cyclic codes of arbitrary length over the class of Galois rings. One such approach involves the use of polynomials with minimal degree corresponding to specific subsets of the code, defined progressively. In this paper, we extend this approach to obtain a set of generators of cyclic codes over finite chain rings. Further, we observe that this set acts as a minimal strong Gröbner basis(MSGB) for the code.
Triage of 2D Mammographic Images Using Multi-view Multi-task Convolutional Neural Networks

With an aging and growing population, the number of women receiving mammograms is increasing. However, existing techniques for autonomous diagnosis do not surpass a well-trained radiologist. Therefore, to reduce the number of mammograms that require examination by a radiologist, subject to preserving the diagnostic accuracy observed in current clinical practice, we develop Man and Machine Mammography Oracle (MAMMO)—a clinical decision support system capable of determining whether its predicted diagnoses require further radiologist examination. We first introduce a novel multi-view convolutional neural network (CNN) trained using multi-task learning (MTL) to diagnose mammograms and predict the radiological assessments known to be associated with cancer. MTL improves diagnostic performance and triage efficiency while providing an additional layer of model interpretability. Furthermore, we introduce a novel triage network that takes as input the radiological assessment and diagnostic predictions of the multi-view CNN and determines whether the radiologist or CNN will most likely provide the correct diagnosis. Results obtained on a dataset of over 7,000 patients show that MAMMO reduced the number of diagnostic mammograms requiring radiologist reading by 42.8% while improving the overall diagnostic accuracy in comparison to readings done by radiologists alone.
Grounded, Controllable and Debiased Image Completion with Lexical Semantics

In this paper, we present an approach, namely Lexical Semantic Image Completion (LSIC) <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> , that may have potential applications in art, design, and heritage conservation, among several others. Existing image completion procedure is highly subjective by considering only visual context, which may trigger unpredictable results which are plausible but not faithful to a grounded knowledge. To permit both grounded and controllable completion process, we advocate generating results faithful to both visual and lexical semantic context, i.e., the description of leaving holes or blank regions in the image (e.g., hole description). One major challenge for LSIC comes from modeling and aligning the structure of visual-semantic context and translating across different modalities. We devise multi-grained reasoning blocks to address this challenge. Another challenge relates to the unimodal biases, which occurs when the model generates plausible results without using the textual description. We devise an unsupervised unpaired-creation learning path that explicitly performs counterfactual thinking, i.e., what the complete image would be if given an unpaired text description to the incomplete image. A cycle consistency loss is devised to guarantee counterfactual faithfulness. We conduct extensive quantitative and qualitative experiments that reveal the strengths of LSIC in being grounded, controllable, and debiased.
A Study on the Construction Features of “Pan X”

From the diachronic and synchronic perspectives, this paper conducts a comprehensive study on the structural features of the online buzzword “pan (盘) X” which has become popular since 2019. At the diachronic level, the “pan (盘) X” construction, in the process of evolving from a grammatical construction to a rhetorical construction, on the one hand, retains the original semantic categories of agentive meaning and causative meaning; on the other hand, it has a high degree of dependency on the specific social environment characteristics of the prescriptive scenes. At the synchronic level, the construction of “pan (盘) X” is flexible in terms of syntactic structure with rich metaphorical meanings and has the semantic feature of controlling. The study on the construction features of “pan (盘) X” shows that there are some similarities between network language and other unconventional languages in essence, and the study of network language represented by “pan (盘) X” construction can deepen our understandings of the nature of network language.
Extracting Implicit Knowledge

The article explores methods for extracting implicit knowledge. It is difficult to transfer implicit knowledge since it is fixed in implicit knowledge medium - a person. The extraction of implicit knowledge transforms it into explicit knowledge and makes it possible to transfer it from person to person. The features of implicit knowledge are described. Implicit knowledge is associated with latent information. Implicit knowledge is interpreted as unknown latent parameters. The article introduces the concept of latent informational situation. A method of constructing a latent informational situation and its gradual transformation into an explicit informational situation is proposed. Explicit informational situation creates explicit knowledge. The method is based on hypothesis building and an incremental approach. The incremental approach is to test the hypothesis elements step by step. If the research stage is completed successfully then proceed to the next stage. If the research phase is not completed successfully then a new hypothesis is put forward. Hypothesis generation and hypotheses stage testing form the basis of the method. The process of scaling as a criterion for latent analysis is described. #CSOC1120.
Heuristic Backward Chaining Based on Predicate Tensorization

Inference methods for first-order logic or its fragments are inherently slow. Neural networks make it possible to rapidly approximate the truth values of ground atoms but the results are not explainable and not necessarily accurate. A hybrid neural-symbolic inference method is proposed in this paper. It is a framework for incorporating predicate tensorization techniques into backward chaining and its extensions. Heuristic functions for best-first search strategies for backward chaining are defined via tensor representations of predicates. These strategies speed up inference by reducing backtracking.
Security and privacy of adolescents in social applications and networks: legislative aspects and legal practice of countering cyberbullying on example of developed and developing countries

Purpose This paper aims to study the developed countries’ experience on the cyberbullying legal regulation among adolescents, to identify existing shortcomings in the developing countries’ laws and to develop recommendations for regulatory framework improvement. Design/methodology/approach The authors have studied the state regulatory practice of the UK, the USA, Canada, Malaysia, South Africa, Turkey, UAE and analyzed the statistics of 2018 on the cyberbullying manifestation among adolescents in these countries. Findings The study results can encourage countries to create separate cyberbullying legislation and periodically review and modify already existing legislation. Originality/value The study provides a list of the recommendations to regulate cybercrime in developing countries and prevent it as well. The results may contribute to creating laws related to the regulation of cyberbullying in countries where such legislation does not exist yet or existing regulatory legal acts do not bring the expected results, namely, in Post-Soviet countries and other developing countries of the world.
Identity is the new perimeter in the fight against supply chain attacks

While cyber security attacks are increasing daily – the financial impact of which is expected to reach $6tr this year alone – one arena gaining more and more attention is supply chain attacks.1 By targeting developers and cloud service providers, threat actors leverage the trust relationships that your organisation has with a range of third parties – including both software vendors and managed service providers.
Energy Aware Load Balancing Algorithm for Upgraded Effectiveness in Green Cloud Computing

Cloud computing leverages the processing force and resources as an administration to the clients present across the world. This plan was acquainted as a route with an end for the customers around the world by giving elite at a more affordable expense in contrast to the devoted high-performance computing machines. For running HPC, business and web applications, cloud computing remains an exceptionally versatile and financially savvy stage. Enormous amount of electrical energy are utilized by server ranches for achieving a high help cost and carbon-di-oxide transmission. The power productivity issues with server ranches are basically a cost of force and cooling structure that has a significant bit of their operative expenses. Energy adequacy is considered as a significant challenge and it is demanding to win in a green answer to deal with the entire patterns that influence the cloud energy utilization. There are four strategies for creating energy effectiveness: equipment level force improvement, energy cognizant planning in lattice structures, server consolidation with the useful resources of virtualization and power minimization. Thus, there is an increasing need for green cloud computing solutions that remains most effective to store power and, however, also reduce operational prices. The imaginative and prescient-based energy efficient control on cloud computing environments is supplied right here. EALB algorithm has been introduced to foresee the heap and incorporate energy productivity in overloaded and under stacked framework.
CHIAPON: An Anthropomorphic Character Notification System that Discourages Their Excessive Smartphone Use

Smartphones have become a central part of information processing devices. Smartphones are used for information searching, taking photographs, listening to music, and socializing. However, as the number of smartphone users continues to expand, a number of problems related to smartphone overuse have emerged. Smartphone overuse is most commonly discouraged by issuing warnings to users or imposing a time limit on usage. However, although these methods discourage smartphone use at the time of issue, simple warnings are ineffective and excessively severe restrictions can frustrate users. This paper proposes a system that messages users through an anthropomorphic character when their smartphone has been overused. The system, called “CHIAPON”, was experimentally evaluated on 25 college students. The results showed that message notification by an anthropomorphic character can improve users’ motivation to reduce their smartphone use, but the extent of this reduction was not clarified. However, the participants using CHIAPON perceived the system positively, suggesting that the effect of the system can increase with long-term use.
The Future of Livestreaming: Strategy and Predictive Analysis for Future Operations of Facebook Live

Social Network Services (SNS) are systems that allow users to build social relations with one another, with one of the largest SNSs being Facebook, totaling 2.6 billion active monthly users in 2020. Their live streaming service, Facebook Live, is one of the fastest-growing branches of the company, allowing creators to synchronously broadcast original content to the public. However, in the rapidly growing world of technology, Facebook Live faces fierce competition from other live streaming platforms (Twitch, YouTube Live, etc.) and well as other video-on-demand providers (Netflix, Hulu, TikTok, etc.). To better understand current issues and future directions, our team focused on the Facebook Live platform, to develop a three to five-year strategic plan for the platform. We focus on Facebook Live’s growth opportunities from a multitude of perspectives, including the competitive landscape, interface modification, future projections based on historical trends, and competitive analysis. Our approach utilizes the systems analysis process, focusing top-down on objectives and metrics. To produce a comprehensive strategy for future operations, we employ analytical methods ranging from quantitative data analysis to qualitative exploration of industry trends. These quantitative methods include statistical analysis, time-series forecasting, and natural language processing. Qualitative methods include domain research into the history, current state, and possible future for live-streaming. Forthcoming, the results for the complete analysis will be synthesized into a multi-recommendation strategic report to provide Facebook with flexible guidance for continuing operations. We also present a comments summarization and visualization feature for viewers and creators, three to five-year market forecasts after COVID-19 lockdowns, and attractive emerging markets including education and morning shows.
A technique for automatically creating a list of terms based on ready-made lesson articles

When constructing a course of study in a discipline, a teacher periodically needs tools for analyzing and visualizing the structure of a future course, in particular, tools for highlighting a system of concepts on the basis of which a future course will be built, as well as for systematizing and structuring this system. The article discusses the problem of automatically compiling a list of concepts (terms) for the subsequent analysis of educational material when creating courses of disciplines. The choice of a system of concepts and methods of its presentation depends on the time frame of the course, the cognitive capabilities of students and their knowledge. The article discusses a method for constructing a thesaurus based on ready-made abstracts of lessons using linguistic methods for analyzing texts in natural language. With the help of graphematic analysis of the abstracts of lessons, the structural units of the course are determined. To compile the thesaurus, the syntactic analysis of the structural units of the text of the “sentence” type is performed for compliance with the template for entering the definition of the concept. To find relationships between concepts, terms from the thesaurus are reduced to all morphological forms and they are searched for in the definitions of other concepts. For the subsequent analysis of the training course, structural units, terms and their relationships are presented in the form of graph models.
Hybrid Fault Tolerance Approach Based on Replication Impact Heuristic for Cloud Computing

Recently fault tolerance is the essence for achieving more reliability, availability and robustness for cloud computing. This paper proposes a hybrid approach based on replication impact heuristic to provide a high level of fault tolerance. This hybrid approach has used reactive and proactive policies. The objective of this work was to used resources efficiently by reduce the consumption of these resources. Our proposed approach has used replication impact heuristic to give each task different replica instead of all tasks have the same replica. And the available resources haven’t used at the same time. Also this approach has based on reliability assessment when re-execute the fail tasks. The CloudSim is a simulator which has used to evaluate the experiments of four scenarios. First scenario has used replication and resubmission technique without any improvement. Second scenario has used replication and resubmission technique based on reliability assessment. Third scenario used hybrid approach based on replication impact heuristic with random injection transient fault injection. Fourth scenario also has used hybrid approach based on replication impact heuristic without fault injection. According to these results, we decide that our approach increases VMS efficiency and guarantee most of tasks are passed during fault occurrence.
Robotic Path Planning Based on Episodic Memory Fusion

Episodic memory provides a mechanism for recalling past experience, which can be used for path planning in complex environments. This paper describes a path planning method based on memory fusion that combines an episodic memory models with the potential path detection network. In traditional path planning methods based on the episodic memory model, paths were planned based on the trajectory that the mobile robot has experienced in the environment, ignoring the surrounding potential paths. Therefore, the planned path is not necessarily globally optimal. In response to this problem, we proposed a path detection network to find potential safe paths in the environment. Our experimental results demonstrated that a better path can be found by fusing the potential path into the original episodic-cognitive map from the perspective of planned path length and number of turns.