Reviewerly/exHarmony huggingface dataset:
Evaluation of effectiveness of chosen-plaintext attacks on the Rao-Nam cryptosystem over a finite Abelian group

The Rao-Nam cryptosystem is a symmetric version of the McEliece code-based cryptosystem proposed to get rid of the shortcomings inherent in the first symmetric code-based encryption schemes. Almost immediately after the publication of this cryptosystem, attacks on it based on selected plaintexts appeared, which led to the emergence of various improvements and modifications of the original cryptosystem.&#x0D; The secret key in the traditional Rao-Nam scheme is a certain Boolean matrix and a set of binary vectors used to generate distortions during encryption. Such vectors must have different syndromes, that is, be different modulo of the code generated by the rows of the specified matrix. The original work of Rao and Nam considered two methods of forming the set of these vectors, the first of which consists in using predetermined vectors of sufficiently large weight, and the second is random selection of these vectors according to the equiprobable scheme. It is known that the first option does not provide the proper security of the Rao – Nam cryptosystem (due to the small number and simple structure of these vectors), but the second option is more meaningful and requires additional research. The purpose of this paper is to obtain estimates of the effectiveness (time complexity for a given upper bound of the error probability) of attacks on a cryptosystem, which generalizes the traditional Rao – Nam scheme to the case of a finite Abelian group (note that the need to study such versions of the Rao – Nam cryptosystem is due to their consideration in recent publications). Two attacks, based on selected plaintext, are presented. The first of them is not mentioned in the works known to the authors of this article and, under certain well-defined conditions, it allows recovering the secret key of the cryptosystem with quadratic complexity.&#x0D; The second attack is a generalized and simplified version of the well-known Struik-van Tilburg attack. It is shown that the complexity of this attack depends on the power of the stabilizer of the set of vectors, which forms the second part of the key, in the translation group of the Abelian group, over which the Rao – Nam cryptosystem is considered. In this paper, a bound is obtained for the probability of triviality of the stabilizer under the condition of random choice of this set. From the obtained bound, it follows that Struik-van Tilburg attack is, on average, noticeably more efficient than the worst case considered earlier.
Latin writing styles analysis with Machine Learning: New approach to old questions

In the Middle Ages texts were learned by heart and spread using oral means of communication from generation to generation. Adaptation of the art of prose and poems allowed keeping particular descriptions and compositions characteristic for many literary genres. Taking into account such a specific construction of literature composed in Latin, we can search for and indicate the probability patterns of familiar sources of specific narrative texts. Consideration of Natural Language Processing tools allowed us the transformation of textual objects into numerical ones and then application of machine learning algorithms to extract information from the dataset. We carried out the task consisting of the practical use of those concepts and observation to create a tool for analyzing narrative texts basing on open-source databases. The tool focused on creating specific search tools resources which could enable us detailed searching throughout the text. The main objectives of the study take into account finding similarities between sentences and between documents. Next, we applied machine learning algorithms on chosen texts to calculate specific features of them (for instance authorship or centuries) and to recognize sources of anonymous texts with a certain percentage.
Did you mix me? Formally Verifying Verifiable Mix Nets in Electronic Voting.

Verifiable mix nets, and specifically proofs of (correct) shuffle, are a fundamental building block in numerous applications: these zero-knowledge proofs allow the prover to produce a public transcript which can be perused by the verifier to confirm the purported shuffle. They are particularly vital to verifiable electronic voting, where they underpin almost all voting schemes with non-trivial tallying methods. These complicated pieces of cryptography are a prime location for critical errors which might allow undetected modification of the outcome.The best solution to preventing these errors is to machine-check the cryptographic properties of the design and implementation of the mix net. Particularly crucial for the integrity of the outcome is the soundness of the design and implementation of the verifier (software). Unfortunately, several different encryption schemes are used in many different slight variations which makes it infeasible to machine-check every single case individually. However, a particular optimised variant of the Terelius-Wikstrom mix net is, and has been, widely deployed in elections including national elections in Norway, Estonia and Switzerland, albeit with many slight variations and several different encryption schemes.In this work, we develop the logical theory and formal methods tools to machine-check the design and implementation of all these variants of Terelius-Wikstrom mix nets, for all the different encryption schemes used; resulting in provably correct mix nets for all these different variations. We do this carefully to ensure that we can extract a formally verified implementation of the verifier (software) which is compatible with existing deployed implementations of the Terelius-Wikstrom mix net. This gives us provably correct implementations of the verifiers for more than half of the national elections which have used verifiable mix nets.Our implementation of a proof of correct shuffle is the first to be machine-checked to be cryptographically correct and able to verify proof transcripts from national elections. We demonstrate the practicality of our implementation by verifying transcripts produced by the Verificatum mix net system and the CHVote e-voting system from Switzerland.
The Visual and Semantic Features that Predict Object Memory

Humans have a remarkable fidelity for visual long-term memory. Much of the work on long-term memory has focused on processes associated with successful encoding and retrieval. However, more recent work on visual object recognition has developed a focus on the memorability of specific visual stimuli. However, studies on object recognition often fail to account for how these high- and low-level features interact to promote distinct forms of memory. Here, we present a novel object database with an extensive array of visual and semantic features assessed for each image, and investigate memory for these object images in two different memory paradigms. We first collected normative feature information on 1000 object images, comprising living and nonliving items spanning 29 different categories. Semantic feature norms were collected and collated to describe complex feature statistics consistent with the conceptual structural account (CSA). Next, we conducted a memory study where we presented these same images during encoding (picture target) on Day 1, and then either a Lexical (lexical cue) or Visual (picture cue) memory test on Day 2. Our findings indicate that higher-level visual factors (via DNNs) and semantic factors (via feature-based statistics) make independent contributions to object memory, and factors that predict object memory depend on the type of memory being tested. These findings help to provide a more complete picture of what factors influence object memorability. Furthermore, the public repository created in this project consists of useful information on the objects including, visual and semantic feature information, memorability of object images in two different memory paradigms, display and creation of multidimensional scaling plots, and downloads for feature and memory data. We hope that this object database will encourage users to interact with various kinds of information and select appropriate cut off points at different levels of novel analyses.
Research on Resource Demand Prediction and Optimal Allocation Method in Cloud Computing Environment

With the continuous development of digital informatization, many data processing technologies came into being. As a new data processing technology, the specific application environment of cloud computing is the whole Internet. Through data processing, analyze and sort out the data correlation, and provide relevant resources and services for users. Operating cloud computing infrastructure plays an important role in realizing the operation of cloud computing, so this paper mainly studies the companies that provide operating cloud computing and related companies that provide cloud computing services. Taking the optimization management of cloud computing resources as the research direction, the short-term dynamic prediction of cloud computing resources of load and other related means is used to conduct specific research. This paper intends to systematically describe cloud computing resources, and then effectively organize and rationally allocate cloud computing resources, improve resource service quality, reduce redundant costs of related enterprises, and achieve the best profit of enterprises.
Few-shot Learning via Dependency Maximization and Instance Discriminant Analysis

We study the few-shot learning (FSL) problem, where a model learns to recognize new objects with extremely few labeled training data per category. Most of previous FSL approaches resort to the meta-learning paradigm, where the model accumulates inductive bias through learning many training tasks so as to solve a new unseen few-shot task. In contrast, we propose a simple approach to exploit unlabeled data accompanying the few-shot task for improving few-shot performance. Firstly, we propose a Dependency Maximization method based on the Hilbert-Schmidt norm of the cross-covariance operator, which maximizes the statistical dependency between the embedded feature of those unlabeled data and their label predictions, together with the supervised loss over the support set. We then use the obtained model to infer the pseudo-labels for those unlabeled data. Furthermore, we propose anInstance Discriminant Analysis to evaluate the credibility of each pseudo-labeled example and select the most faithful ones into an augmented support set to retrain the model as in the first step. We iterate the above process until the pseudo-labels for the unlabeled data becomes stable. Following the standard transductive and semi-supervised FSL setting, our experiments show that the proposed method out-performs previous state-of-the-art methods on four widely used benchmarks, including mini-ImageNet, tiered-ImageNet, CUB, and CIFARFS.
THE IMPLEMENTATION OF CARD SORT STRATEGY IN INCREASING STUDENTS’ ABILITY TO USE ADVERB IN SENTENCE AT MTs MUHAMMADIYAH KISARAN

This study was conducted to find out the implementation of Card Sort strategy in increasing students’ ability to use Adverb in Sentence. The population of this study was the grade VIII students of MTs Muhammadiyah 2019/ 2020 academic years. The writer used cluster random sampling, there were 32 students as the samples And this research applied by classroom action research. The technique of analyzing data of this study was applied by using qualitative and quantitative data. The qualitative data were taken from interview, observation sheet, and document. The quantitative data were taken from the test. The result of the analysis showed that the mean of the first cycle was 59.37. The mean of second cycle was 78.12. The percentage of students who got point up to 65 also grew up. In the pre-test, there was only 15.62% (five students) who got point 65 above. In the post test of cycle I, there was 59.37% (nineteen students) who got point 65 above. It means that there was improvement about 43.75%. In the post test of cycle II, there was 78.12% (twenty one students) who got points 65 above. The improvement was about 18.75%. The total improvement of the students’ score from the pre-test to the post test of cycle II was 62.5%. It indicated that the implementation of Card Sort strategy was effective as it could increase students’ ability to use Adverb in Sentence
Modern Visual Poetry and Ecology: Towards Establishing “Eco-Visual Poetry” As A New Poetic Genre

In no other age in the history of literature did poetry witness this great change as much as it does in ours. Poetry moves indiscriminately towards visuality, fragmentation, and digitalization. This change has led to the appearance of modern visual poetry as an individual poetic production with specific characteristics and techniques after years of being a mere experimental poetic activity.  Modern visual poetry is the mirror of the contemporary society which has declared the death of postmodernism and the appearance of new era of fragmentation, chaos, hyper-reality and techno culture. The new era is always concerned with ecology as one of the most important issues. That is why, the present paper aims at presenting a new subclass of visual poetry that is eco-visual poetry. The paper discusses eco-visual poetry as a new born genre of poetry that is associated with visuality, ecology, and literature. Thus, it defines eco-visual poetry as a new genre of poetry
Experimental Study of Heat Transfer Rate in Single and Series Cross Flow Heat Exchanger using Matlab Coding

The present study investigates the heat transfer rate and effectiveness of cross flow heat exchanger by varying velocity and mass flow rate of the cold and hot fluids.The velocity of the cold fluid i.e. air varying from 15m/s to 30m/s(with an intermediate step of 20m/s and 25m/s) whereas the mass flow rate of hot fluid i.e. water is taken as 35lit/hr and 84lit/hr. The logarithmic mean temperature difference (LMTD) method is used to find the heat transfer rate. In thepresent workthe effectiveness and heat transfer rate werecompared between the single and series cross flow heat exchangers. The result shows an average increase of 47.14% and 59.59% of heat transfer rate analogous to mass flow rate of 84lit/hrand 35lit/hr
IMPROVING STUDENTS’ MASTERY OF VOCABULARY THROUGH THE USE OF GLOSSING TECHNIQUE ( A Study conducted at SMP Anugrah Tondano )

The aim of the research is to improve the students’ vocabulary through the use of Glossing technique. The design of this research is quantitative research through pre-experimental design with pre-test and post-test design This study was conducted at SMP Anugrah Tondano. The subject of the research was the second grade students in academic year of 2019/2020. The writer used reading text of Narrative text .The result of the data analysis showed that there was an increase between the pre-test and post-test. It can be seen from 10 students showed that the mean score of pre-test was 53 and post-test was 74 score. Based on this finding from this study, it can be concluded that having more vocabulary significantly affects the comprehension of the students reading activity. Moreover by having many vocabularies the students can easily get the information and can enrich their knowledge by reading a lot of book. Finally, this research proved that theGlossing technique effective to improve student’s vocabulary in reading comprehension.
Counterfactual Evaluation for Explainable AI

While recent years have witnessed the emergence of various explainable methods in machine learning, to what degree the explanations really represent the reasoning process behind the model prediction -- namely, the faithfulness of explanation -- is still an open problem. One commonly used way to measure faithfulness is \textit{erasure-based} criteria. Though conceptually simple, erasure-based criterion could inevitably introduce biases and artifacts. We propose a new methodology to evaluate the faithfulness of explanations from the \textit{counterfactual reasoning} perspective: the model should produce substantially different outputs for the original input and its corresponding counterfactual edited on a faithful feature. Specially, we introduce two algorithms to find the proper counterfactuals in both discrete and continuous scenarios and then use the acquired counterfactuals to measure faithfulness. Empirical results on several datasets show that compared with existing metrics, our proposed counterfactual evaluation method can achieve top correlation with the ground truth under diffe
Detection and Continual Learning of Novel Face Presentation Attacks.

Advances in deep learning, combined with availability of large datasets, have led to impressive improvements in face presentation attack detection research. However, state-of-the-art face antispoofing systems are still vulnerable to novel types of attacks that are never seen during training. Moreover, even if such attacks are correctly detected, these systems lack the ability to adapt to newly encountered attacks. The post-training ability of continually detecting new types of attacks and self-adaptation to identify these attack types, after the initial detection phase, is highly appealing. In this paper, we enable a deep neural network to detect anomalies in the observed input data points as potential new types of attacks by suppressing the confidence-level of the network outside the training samples' distribution. We then use experience replay to update the model to incorporate knowledge about new types of attacks without forgetting the past learned attack types. Experimental results are provided to demonstrate the effectiveness of the proposed method on two benchmark datasets as well as a newly introduced dataset which exhibits a large variety of attack types.
Aligning Hotel Embeddings using Domain Adaptation for Next-Item Recommendation

In online platforms it is often the case to have multiple brands under the same group which may target different customer profiles, or have different domains. For example, in the hospitality domain, Expedia Group has multiple brands like Brand Expedia, Hotels.com and Wotif which have either different traveler profiles or are more relevant in a local context. In this context, learning embeddings for hotels that can be leveraged in recommendation tasks in multiple brands requires to have a common embedding that can be induced using alignment approaches. In the same time, one needs to ensure that this common embedding space does not degrade the performance in any of the brands. In this work we build upon the hotel2vec model and propose a simple regularization approach for aligning hotel embeddings of different brands via domain adaptation. We also explore alignment methods previously used in cross-lingual embeddings to align spaces of different languages. We present results on the task of next-hotel prediction using click sessions from two brands. The results show that the proposed approach can align the two embedding spaces while achieving good performance in both brands. Additionally, with respect to single-brand training we show that the proposed approach can significantly reduce training time and improve the predictive performance.
Development Education Game Based VAR (Virtual Augmented Reality) In Kindergarten Semarang City

In making educational game designs that fit the needs of children, an in-depth study is needed, one of them is the way of thinking of the child, in this research an educational game media will be designed that is suitable for the kindergarten children's thinking, we can first make changes to ADDIE analysis model, consist of design, development, dissemination, in the analysis steps of the analysis of the needs analysis with the teacher and children of kindergarten PGRI 73 Semarang added (1) choosing material according to the thematic, (2) the use of media play in class, (3 ) adjusted the way of thinking of children according to the theory of brunettes that is enactive, econic and symbolic performance learning, (4) educational games must be packaged in the form of pictures and voice commands only kindergarten children can not read, this is as a basis in making media design games education, the next step is to design an educational game media with the following design steps: (1) determining the material / material that you want to be included in the game (2) arrange the Game Play (story line / the player's journey while in the game), (3) create 3D assets such as trees, rocks, clouds, land and related objects using 3D software using Blender Software, (4) Making games (here use Unity Game Engiene) arranging all assets according to the gameplay gameplay that has been compiled, (5) Activating the Vuforia Plugin which provides a library that is used to create VR displays, (6) Programming game controls using Bluetooth Game Pad so that users can control the game, (7) Build applications, (8) Test the Bug, after the design of the educational game, then proceed with the validation of material experts with an average rating of 89, 71 and media experts with an average value of 92.31 means that the media is appropriate to use.
ATTITUDES OF FOURTH GRADE GYMNASIUM STUDENTS WHO ARE NOT IN MATHEMATICS AND INFORMATICS COURSE IN TERMS OF DESCRIPTIVE GEOMETRY AS A POSSIBLE COMMON SUBJECT IN ALL DIRECTIONS OF GYMNASIUM

Gymnasium as a school and as one of the levels in education has changed for decades both in the curriculum and in its duration. Nevertheless, the common goal in each period of Gymnasium education was and remains to provide students with the widest possible general education and to prepare them for further education at various universities of technical, social and natural sciences. In the last stage of socialism in Bosnia and Herzegovina, all high schools in their curriculum aimed to train students for one of the working professions so that each student after graduating from high school acquired a certain knowledge and could be employed in the sector for which educated. During that period, grammar schools were formally abolished. Instead, secondary administrative schools were most often formed, which were most similar to Gymnasiumin terms of their curriculum. In the present age, the gymnasium as a school exists with the fact that the curriculum is common to all first and second grades, while the third and fourth grades are divided into directions: mathematics-informatics, natural, social, linguistic and information-communication. Without going into the purpose of the existence of other directions, it should be emphasized that the mathematics-informatics direction aims to bring the students of the final grades of grammar school closer to technical and informatics universities, ie to acquaint them with technical and informatics. One of the key subjects at some technical colleges is descriptive geometry. These would primarily be the universities of architecture, civil engineering and mechanical engineering. In informal conversations between fourth graders regardless of direction and their teachers from time to time the topic is the subject of descriptive geometry. From the mentioned conversations, two mutually opposing hypotheses crystallized in terms of the importance of descriptive geometry, ie whether or not descriptive geometry should be introduced in all directions of Gymnasium. In order to determine which of these two hypotheses prevails, a generic / developmental method was applied, ie a survey was used as one of the research techniques. The survey was conducted in February 2020. A sample of 80 fourth-grade students from the „Muhsin Rizvić“ Gymnasium in Kakanj and the „Visoko“ Gymnasium in Visoko, who are not in the mathematics and computer science trend, was selected for the survey. As can be seen, the importance of descriptive geometry as a subject will be expressed by those students who do not have descriptive geometry as a subject according to the curriculum