Reviewerly/exHarmony huggingface dataset:
(Public) Verifiability for Composable Protocols Without Adaptivity or Zero-Knowledge

The Universal Composability (UC) framework (FOCS ’01) is the current standard for proving security of cryptographic protocols under composition. It allows to reason about complex protocol structures in a bottom-up fashion: any building block that is UC-secure can be composed arbitrarily with any other UC-secure construction while retaining their security guarantees. Unfortunately, some protocol properties such as the verifiability of outputs require excessively strong tools to achieve in UC. In particular, “obviously secure” constructions cannot directly be shown to be UC-secure, and verifiability of building blocks does not easily carry over to verifiability of the composed construction. In this work, we study Non-Interactive (Public) Verifiability of UC protocols, i.e. under which conditions a verifier can ascertain that a party obtained a specific output from the protocol. The verifier may have been part of the protocol execution or not, as in the case of public verifiability. We consider a setting used in a number of applications where it is ok to reveal the input of the party whose output gets verified and analyze under which conditions such verifiability can generically be achieved using “cheap” cryptographic primitives. That is, we avoid having to rely on adaptively secure primitives or heavy computational tools such as NIZKs. As Non-Interactive Public Verifiability is crucial when composing protocols with a public ledger, our approach can be beneficial when designing these with provably composable security and efficiency in mind.
Qos Aware Selection of Servers for Iaas Providers: A Trust Model Perspective

Cloud Computing is a computing as a service over the internet. From software applications to development platforms, to computing infrastructures, all these are available as services. Hence, Quality-of-Service (QoS) comes into existence for playing a major role in this domain. This paper proposes a QoS aware selection of servers using QoS Trust Manager (QTM) module. QTM calculates the customer specific trust score for each server before allocation. The assessments are based on server throughput, server response time and tasks execution time using various scheduling algorithms. The simulations of the proposed work were carried out to examine its features and practicability. The proposed method, aims to improve the quality of server selection procedure and performance enhancement.
When Do Entrepreneurial Firms Benefit From Transactive Memory Systems

Despite the recognized importance of transactive memory systems (TMS) for firm performance, this relationship remains misconstrued for entrepreneurial firms. Some researchers argue that entrepreneurial firms benefit from effective knowledge management systems, whereas others contend that such systems may prove expensive for resource-scarce entrepreneurial firms. Accordingly, the purpose of this paper is to explore this relationship in the context of Indian entrepreneurial firms. Furthermore, the authors argue that relationship conflict among organizational members impacts the TMS–performance link for entrepreneurial firms.Design/methodology/approach - Data were collected from 127 entrepreneurial hotels in the Uttarakhand state of India and analyzed using multiple linear regression.
Use of Online Learning Media in Family Hope Program (PKH) Training at BBPPKS Regional 1 Sumatera

This research was motivated by high learning outcomes in PKH training at BBPPKS Regional 1 Sumatera. It is proven by the learning outcomes of participants who reach the graduation target set by the BBPPKS institution. Researchers suspect that the cause of high training participants learning outcomes is because Widyaiswara and participants are able to use online learning media well. This study aims to examine the use of online learning media in the Family Hope Program (PKH) training at BBPPKS Regional 1 Sumatera. This type of research is descriptive quantitative research. The population in this study was PKH training participants, totaling 40 people. The sample was taken from 65% of the population, and the sample technique was cluster random sampling. The data collection technique uses a questionnaire, while the data collection tool lists statements. The data analysis technique uses the percentage formula. The results showed that the use of online learning media in PKH training at BBPPKS Regional 1 Sumatera was (a) the relevance of the material for using online learning media was good (b) Widyaiswara’s ability to use online learning media was good (c) ease of use of online learning media for participants classified as good (d) the availability of materials using online learning media is classified as good (e) the usefulness of using online learning media is classified as good. It is proven by most of the training participants giving statements always.
19. századi magyar regényeken végzett kísérletek a magyar nyelvű szerzőazonosítás leghatékonyabb távolságméréseinek megtalálására

In the field of computational stylistics it is a widespread assumption that there exists a unique pattern of a person’s language use and this is a socalled authorial fingerprint (Baayen et al. 2002). Identifying the authorial fingerprint can become a base of using quantitative text similarity studies for authorship attribution. Although the metaphor of the “fingerprint” may give the false impression that this pattern can be read from the author’s texts in an objective way. The modelling of this “fingerprint” is in fact a creative digital humanities task which constructs a pattern that is based on a selection and combination of dozens of linguistic features that can be interpreted statistically and that can only be interpreted in comparison with other authorial texts. Considering the size of the corpora and the complexity of text characteristics and similarity calculations, the method cannot be done without the use of computer algorithms. There are already some softwares (e.g. R-Stylo, JGAAP, Websty) that apparently offer an accessible way to researchers for analyzing texts, (however these methods limit the process of searching for patterns), while on the other hand it is possible to implement the calculations using custom program codes. In our research we endeavour to find the most efficient measures in authorship attribution for Hungarian texts.
Post-Quantum Cryptography: Challenges and Opportunities for Robust and Secure HW Design

Post-Quantum Cryptography (PQC) will become soon the standard for many systems of the future. With the advent of quantum computers, all encrypted communications based on traditional asymmetric cryptography (e.g., RSA, ECC) will become insecure. The definition the PQC standards is an on going process proceeding at a fast pace, involving new and largely unexplored cryptographic primitives. For this reason, the design of hardware implementations of PQC algorithms is still under study. In this paper, we introduce the fundamentals of PQC, with a focus on lattice-based cryptography and its hardware security issues, namely side-channel and fault-based attacks. Then, we focus on isogeny-based cryptography and the SIKE algorithm. We highlight the importance of fault-tolerant design choices through the presentation of a fault attack, based on the electromagnetic injection of transient faults, targeting this cryptographic primitive. Finally, we show an interesting idea that starts from the observation that some PQC algorithms have an intrinsic probabilistic behavior. We argue that this characteristic is a clear opportunity that paves the way for the application of approximate (or inexact) computing to the implementation of PQC cryptography.
THE PHENOMENON OF “COMPETENCE”: ASSESSMENT OF THE COMPETENCIES OF MANAGERS (FACTORS OF PREFERENCE)

Background. The study of the application of the competence-based approach in the professional training of specialists in the management sphere remains an urgent issue for many researchers, psychologists, educators and teachers. We began our consideration of this problem in a first approximation in a material published in the previous issue of the journal (Tolochek, 2020). The main issue is to identify the limitations of the competence-based approach as factors of its development and the conditions under which its implementation will be more constructive and effective. It is stated that, in line with the competence-based approach, there remains unresolved issues about the number and content of the qualities of subjects that affect the effectiveness of their activities; on the competencies of subjects performing different labor functions, working in organizations; about the place of competences in the structure of professionalism, about their role in career success. Design: expert surveys, a differentiated and level analysis of frequency matrices of assessments (choices) of competencies by experts. Results. The cycle of studies of competencies and the specifics of their assessment included 69 managers differing in gender, age, seniority, managerial experience, positions in different companies. It was shown that out of 29 competencies, 19 were identified in at least 40% of experts; 8 were identified in at least 66% of experts. Competencies assessed as in demand are associated with individual features, with the evolution of managers as subjects and the characteristics of their activities. Conclusions: 1. Only part of the competencies from the initial list of competencies described by foreign specialists were identified as relevant for managers by experts (employees of Russian companies). Among the competences, there is a “core” of basic, key qualities, and several “belts” (“shells”, “levels”) — less significant (universal) competencies associated with the effective activity of a manager in different areas. 2. Assessments of competencies relevant (important, significant and / or preferred) for managers differ depending on the individual characteristics of experts (gender, age, experience, position, career success, managerial potential) and the length of the list of assessed qualities. 3. Lists of competencies in the range of 15-20 qualities can be considered optimal for solving both scientific and applied problems. The basic list of 29 competencies can be considered “sufficient and redundant”; list of up to 19–20 competencies — “sufficient and necessary”; a list of 8–10 “nuclear” competencies — “essential basic”.
The Inflation Period and the Search for Dark Matter

This summer PBL is designed to help us understand the formation of the universe, as well as the formation of galaxies.We spent the class time going through vocabulary words, the stages of universe expansion, signs of the big bang, researching dark matter, dark energy, the theory of relativity, etc.During our final session of class time, we researched a lot of information about the inflation period, specifically how the inflation period solved 4 major issues, as well as 3 different experiments that are searching for dark matter.In this write-up, I am going to summarize and explain all of the research we did.
THE PERSON WHO LEFT HIS FOOTPRINT ON THE EARTH (THE LEGACY OF A.D. SMETANNIKOV)

A person cannot live without a name, without a Motherland, without a history. History is our present, our future and, of course, our past. The time passing away from us leaves a memory of itself in the form of objects, documents, photographs. To collect all this, preserve and present it to the next generations, museums are needed. In 1980, in the regional center of the Troitskoye settlement, a significant event took place, dedicated to the 110th anniversary of the birth of VI. Lenin - the opening of a national museum. The organizer, who collected the richest material on the history of the Troitsky region, was a teacher, historian, local historian - an amateur and enthusiast of his work Smetannikov Alexander Danilovich. The article is devoted to the issues of preserving cultural and historical heritage, the mission of the custodian of historical values in the museum. The article discusses the definition of historical memory and its content. The memory of a man who made a huge contribution to the preservation of the cultural and historical heritage and is the most important part of the historical memory of the inhabitants of the Troitsky District. Keywords: museum organization, teacher, school, heritage, director
Advancing Causal Inference: Differences-in-Differences vs. Bayesian Structural Time Series Models

As competition becomes fierce and competitive environments change more dynamically than ever, understanding the implications of any strategic decision has become important for firms to maximize the effect of their resource allocation. Thus, evaluating the causal inference of strategy implementation is critical for strategists, yet few studies address practitioners’ needs in this manner. While a few studies have prioritized the need for causal inference by employing the differences-in-differences (DD) method in management research, the inherit limitations of this method pose problems for the suitability and reliability of its results. Thus, this study aims to introduce a new approach for causal inference in management research, the Bayesian structural time series (BSTS) model. The BSTS analysis enables us to investigate causal inference from existing whether or not to when and how long. Using customer data from a Korean e-commerce platform, we show why BSTS can be superior to DD thereby providing a basis for subsequent use of causal inference studies delivering practical management implications.
CONSTRUCTIVIST APPROACH IN TEACHING INFORMATION AND SEARCH ACTIVITIES TO HIGH SCHOOL STUDENTS

The ability to search for information effectively is one of the key skills of a person in the modern society. A general trend in the development of educational systems is their reorientation to the formation of self-education skills, training strategies for working with information. The purpose of the article is to substantiate the possibility of using the potential of the constructivist approach in teaching information and search activities to high school students. In the context of the constructivist approach, the concepts of “information search activity” are clarified, the pedagogical conditions for the formation of the ability to carry out information search are developed and justified. The authors describe the structure of readiness for self-organization of information search activity, it includes motivational, procedural and reflexive components, as well as evaluation and diagnostic tools for studying the level of the formation of high school students' readiness for self-organization of information search. The article describes a model developed by the authors on the basis of the ideas of pedagogical constructivism and metacognitive learning for the formation of high school students ' readiness for self-organization of information and search activities, which is based on the integration into the educational process of a set of special exercises and optional tasks, testing and reflection of their own information and search activities. Within the framework of the experiment, methods of problem-based, project-based, and reflexive learning, as well as teaching metacognitive strategies for interacting with information, were implemented into the subjects of the social and arts cycle. The conducted experimental research indicates the possibility of forming the readiness of high school students to self-organize information and search activities on the basis of teaching metacognitive strategies in working with information (search, selection, comprehension, development, transformation) in the process of studying the disciplines of the social and arts cycle in the comprehensive school.
Hessian-Aided Random Perturbation (HARP) Using Noisy Zeroth-Order Oracles

In stochastic optimization problems where only noisy zeroth-order (ZO) oracles are available, the Kiefer-Wolfowitz algorithm and its randomized counterparts are widely used as gradient estimators. Existing algorithms generate the random perturbations from certain distributions with a zero mean and an isotropic (either identity or scalar) covariance matrix. In contrast, this work considers the generalization where the perturbations may have an anisotropic covariance based on the ZO oracle history. We propose to feed the second-order approximation into the covariance matrix of the random perturbation, so it is dubbed as Hessian-aided random perturbation (HARP). HARP collects two or more (depending on the specific estimator form) ZO oracle calls per iteration to construct the gradient and the Hessian estimators. We prove HARP’s almost-surely convergence and derive its convergence rate under standard assumptions. We demonstrate, with theoretical guarantees and numerical experiments, that HARP is less sensitive to ill-conditioning and more query-efficient than other gradient approximation schemes whose random perturbations have an isotropic covariance.
Neural Lexicon Reader: Reduce Pronunciation Errors in End-to-end TTS by Leveraging External Textual Knowledge

End-to-end TTS requires a large amount of speech/text paired data to cover all necessary knowledge, particularly how to pronounce different words in diverse contexts, so that a neural model may learn such knowledge accordingly. But in real applications, such high demand of training data is hard to be satisfied and additional knowledge often needs to be injected manually. For example, to capture pronunciation knowledge on languages without regular orthography, a complicated grapheme-to-phoneme pipeline needs to be built based on a large structured pronunciation lexicon, leading to extra, sometimes high, costs to extend neural TTS to such languages. In this paper, we propose a framework to learn to automatically extract knowledge from unstructured external resources using a novel Token2Knowledge attention module. The framework is applied to build a TTS model named Neural Lexicon Reader that extracts pronunciations from raw lexicon texts in an end-to-end manner. Experiments show the proposed model significantly reduces pronunciation errors in low-resource, end-to-end Chinese TTS, and the lexicon-reading capability can be transferred to other languages with a smaller amount of data.
TAG: Toward Accurate Social Media Content Tagging with a Concept Graph

Although conceptualization has been widely studied in semantics and knowledge representation, it is still challenging to find the most accurate concept terms to tag fast-growing social media content. This is partly attributed to the fact that most traditional knowledge bases contain general terms of the world, such as trees and cars, which are not interesting to users, and do not have the defining power for social media content. Another reason is that the intricate use of tense, negation and grammar in social media content may change the logic or emphasis of the content, thus focusing on different main ideas. In this paper, we present TAG, a high-quality concept matching dataset consisting of 10,000 labeled pairs of fine-grained concepts and web-styled natural language sentences, mined from open-domain social media content. The concepts we provide are the trending terms on social media and have the right granularity to define user interests, e.g., highly educated actors instead of just actors. In the meantime, TAG offers a concept graph which interconnects these fine-grained concepts and entities to provide contextual information. We evaluate a wide range of neural text matching models as well as pre-trained language models for the concept matching task on TAG, and point out their insufficiency to tag social media content to characterize its main idea. We further propose a novel graph-graph matching framework that demonstrates superior abstraction and generalization performance by better utilizing both the structural information in the concept graph and logic interactions between semantic units in the natural language sentence via syntactic dependency parsing.
MODELISASI KOTAK TISU DENGAN PENGGABUNGAN KURVA BEZIER, KURVA HERMIT DAN HASIL DEFORMASI BENDA GEOMETRI

The tissue box is a place to store tissues to make them look neat and protect the tissues from dirt and dust. Tissue boxes are often used in households, restaurants and also as room decorations. Therefore, the shapes of tissue boxes that are being developed are increasingly varied according to consumer interests. The tissue box consists of three main parts, namely the cover, body and base of the box. This research was carried out by developing variations in the shape of the tissue box components using the Bezier curve, the Hermit curve and the results of the deformation of geometric objects. The deformation techniques used are rotation, dilation, and curve interpolation. Tissue box modeling processes are divided into four stages. The first, modeling the tissue box by dividing into three models, namely model A, model B and model C. The second, determining the size of the tissue box components based on the model. The third, modeling tissue box components. Finally, visualizing the results of the tissue box model by combining the components so that a variety of tissue box models are produced.
Can a Robot Trust You? : A DRL-Based Approach to Trust-Driven Human-Guided Navigation

Humans are known to construct cognitive maps of their everyday surroundings using a variety of perceptual inputs. As such, when a human is asked for directions to a particular location, their wayfinding capability in converting this cognitive map into directional instructions is challenged. Owing to spatial anxiety, the language used in the spoken instructions can be vague and often unclear. To account for this unreliability in navigational guidance, we propose a novel Deep Reinforcement Learning (DRL) based trust-driven robot navigation algorithm that learns humans’ trustworthiness to perform a language guided navigation task.Our approach seeks to answer the question as to whether a robot can trust a human’s navigational guidance or not. To this end, we look at training a policy that learns to navigate towards a goal location using only trustworthy human guidance, driven by its own robot trust metric. We look at quantifying various affective features from language-based instructions and incorporate them into our policy’s observation space in the form of a human trust metric. We utilize both these trust metrics into an optimal cognitive reasoning scheme that decides when and when not to trust the given guidance. Our results show that the learned policy can navigate the environment in an optimal, time-efficient manner as opposed to an explorative approach that performs the same task. We showcase the efficacy of our results both in simulation and a real world environment.