Reviewerly/exHarmony huggingface dataset:
Performance Analysis of Idea Algorithm on FPGA for Data Security

Data security is important issue in computer networks. Cryptographic algorithms are essential parts in network security. There are plenty of algorithms for the purpose of security. IDEA is one among the proposed algorithm. International Data Encryption Algorithm is symmetric encryption algorithm which is widely used for the purpose of security with simple arithmetic operation. This paper presents an FPGA implementation of IDEA encryption and decryption using Verilog HDL on Spartan 3. It is further implemented for image encryption and decryption using MATLAB. The usage of 128-bit key control makes this algorithm secure and reliable. Experimental synthesis result shows that the proposed design is better where, the delay is reduced to 212.498ns and only 1% of available memory resource is used.
Exploring the Soft System Methodology in Development of Knowledge Management Conceptual Model a Sytematic Literature Review

&#x0D; &#x0D; &#x0D; Soft System Methodology (SSM) is common guidance to conceptualise Knowledge Management (KM) model since its benefit to handle complex situations. This study highlights SSM implementation on KM conceptual model using Systematic Literature Review (SLR) to produce insights about current implementation and promising chances in the future. This study also concerns on KM conceptual models as created without certain features in many case studies. Many various interpretation can arise to understand how system should work. This situation affected more spending time to validate KM conceptual model with stakeholders. Using SLR, this study criticises the importance of features as provided in eligible articles. Out of 144 articles from 2009 until 2019 as delivered from Scopus query, this study selected 12 articles about SSM on KM. It classified them into several criteria: published year, sector, and related KM life cycle. By mapping them to solve research questions, this study promoted actors, activities, and sequential order of activities as recommended features. By implementing them, KM conceptual model creation can be faster and accepted by stakeholders.&#x0D; &#x0D; &#x0D;
Some permutations and complete permutation polynomials over finite fields

In this paper we determine $b\in\F_{q^n}^*$ for which the polynomial $f(x)=x^{s+1}+bx\in\F_{q^n}[x]$ is a permutation polynomial and determine $b\in\F_{q^n}^*$ for which the polynomial $f(x)=x^{s+1}+bx\in\F_{q^n}[x]$ is a complete permutation polynomial where $s=\frac{q^n-1}{t}, t\in \mathbb{Z}^+$ such that $t\mid q^n-1$.
Ultrasound Speckle Noise Reduction by Radio-Frequency Data Filtering

Ultrasound is a commonly used imaging modality for the examination of several pathologies due to its non-invasiveness, affordability and easiness of use. However, ultrasound images are degraded by an intrinsic artefact called ‘speckle’, which is the result of the constructive and destructive coherent summation of the ultrasound echoes. This paper aims to generate B-mode images out of radio-frequency (RF) data following standard procedures, a series of steps such as envelope detection, log-compression and scan conversion. Some low pass filters will be applied to RF data in order to achieve B-mode images with high quality by speckle noise reduction.
Cryptocurrency Egalitarianism: A Quantitative Approach

Since the invention of Bitcoin one decade ago, numerous cryptocurrencies have sprung into existence. Among these, proof-of-work is the most common mechanism for achieving consensus, whilst a number of coins have adopted as a desirable property, claiming to be more where egalitarianism refers to the power of each coin to participate in the creation of new coins. While proof-of-work consensus dominates the space, several new cryptocurrencies employ alternative consensus, such as proof-of-stake in which block minting opportunities are based on monetary ownership. A core criticism of proof-of-stake revolves around it being less egalitarian by making the rich richer, as opposed to proof-of-work in which everyone can contribute equally according to their computational power. In this paper, we give the first quantitative definition of a cryptocurrency’s egalitarianism. Based on our definition, we measure the egalitarianism of popular cryptocurrencies that (may or may not) employ ASIC-resistance, among them Bitcoin, Ethereum, Litecoin, and Monero. Our simulations show, as expected, that ASIC-resistance increases a cryptocurrency’s egalitarianism. We also measure the egalitarianism of a stake-based protocol, Ouroboros, and a hybrid proof-of-stake/proof-of-work cryptocurrency, Decred. We show that stake-based cryptocurrencies, under correctly selected parameters, can be perfectly egalitarian, perhaps contradicting folklore belief.
Holistic analysis of the Vehicle Routing Problem: an approach for GIS-T

Urban cargo transport movements, due to their characteristics and particularities, involve different dimensions and variables.As a result, it demands specific logistics costs, consisting of a relevant portion of the total business costs, being directly linked to the route taken by the vehicle fleet.In that way, it is necessary to define roadmaps for vehicles in order to minimize the total distance traveled, total cost and total elapsed time when supplying clients.With the development of hardware and software in the 90's, complex Vehicles Routing Problems (VRP's) (containing multiple clients and vehicles) started to being solved by iterative mathematical methods.Thus, this article aim to solve the VRP's of a garlic-based products fabricate company using a Geographic Information System for Transportation (GIS-T), performing comparative analysis with the current company operations and presenting alternative scenarios that may provide any cost reduction.
Osseous Fracture Patterns: From CT to a Plane Representation

The extraction of bone fracture patterns for a posterior reproduction of the fracture on different bone models is almost an unexplored field of research. These templates can recreate a similar fracture on other areas or bones, with applications on medical training of traumatologist and automatic bone fracture reduction algorithms. This paper is focused on the process of generating a fracture pattern taking computed tomography scans as a starting point. In addition, several representations of the templates, for different purposes, are presented and discussed. Finally, the potential uses of the bone fracture patterns are described.
Acquisition of English Tense and Aspect Translation Competence by Behdini Students: An Experimental Study

This study is an attempt to investigate the acquisition of translation competence of the English tense and aspect system by Behdini learners who are students at the Translation Department at the College of Languages in the University of Duhok. This paper is an experimental study that adopts the Translation Competence Acquisition model. There are many morphological and syntactic differences between English and Behdini tense and aspect and there are differences in terms of the usage of the tense and aspect between the two mentioned languages too. A Judgement Elicitation Task is employed as a tool to collect data in this study. 4o English sentences with their translations into Behdini are included in this task. Behdini learners are asked to make their judgements on each translated sentence. These test items are a mixture of four tenses: present continuous, present perfect, past continuous, and past perfect. Two subgroups of learners are involved in this study: the senior subgroup and the fresher subgroup in an attempt to investigate the effect of participants’ English language level and proficiency. Mixed-effects modeling has been used for analysing the data statistically. The lmer package (version is 3.3.1) has been employed with logit link function and binomial variance for the judgement data in R, which is an open-source language and environment for statistical computing. The main hypothesis of the study is that Behdini learners are not expected to attain a complete translation competence regarding the English tense and aspect system due to the differences between the two languages. The main results of the study show that while Behdini learners were able to attain a good translation competence in terms of accepting the grammatical translations, they failed to reject the ungrammatical translations. These findings implicate that Behdini learners’ acquisition of translation competence is not attained fully. It is also shown that Behdini students at lower proficiency levels employ their L1 grammar as the first stage of their translation process, but at later stages of proficiency parameter resetting becomes more possible.
Extended Learning Graphs for Triangle Finding

We present new quantum algorithms for Triangle Finding improving its best previously known quantum query complexities for both dense and sparse instances. For dense graphs on n vertices, we get a query complexity of $$O(n^{5/4})$$ without any of the extra logarithmic factors present in the previous algorithm of Le Gall [FOCS’14]. For sparse graphs with $$m\ge n^{5/4}$$ edges, we get a query complexity of $$O(n^{11/12}m^{1/6}\sqrt{\log n})$$, which is better than the one obtained by Le Gall and Nakajima [ISAAC’15] when $$m \ge n^{3/2}$$. We also obtain an algorithm with query complexity $${O}(n^{5/6}(m\log n)^{1/6}+d_2\sqrt{n})$$ where $$d_2$$ is the quadratic mean of the degree distribution. Our algorithms are designed and analyzed in a new model of learning graphs that we call extended learning graphs. In addition, we present a framework in order to easily combine and analyze them. As a consequence we get much simpler algorithms and analyses than previous algorithms of Le Gall et al. based on the MNRS quantum walk framework [SICOMP’11].
Procedural Progression Model for Smash Time

This paper addresses the problem of improving the player experience in single player endless games and encouraging the player to be engaged with procedurally generated content for longer periods of time. We present a model in which the procedural content generation process takes into consideration the dynamics of two dimensions of the player experience: the performance of the player when overcoming the challenges created by the game, and the variety of challenges presented to the player over time. We discuss the implementation of the model in the endless mode of the mobile game Smash Time, and describe how its evaluation supports that the model was able to increase both the number and duration of play sessions as well as having a better game experience reported by the participants, when compared to the original game. These results suggest that this approach could improve replayability and, as a consequence, the lifetime of a digital game.
Breast Cancer Detection using Deep Learning

Accurate diagnosis of breast cancer in histopathology images is challenging due to the heterogeneity of cancer cell growth as well as of a variety of benign breast tissue proliferative lesions. In this work, we propose a practical and self interpretable invasive cancer diagnosis solution. With minimum annotation information, the proposed method mines contrast patterns between normal and malignant images in unsupervised manner and generates a probability map of abnormalities to verify its reasoning. Particularly, a fully convolutional autoencoder is used to learn the dominant structural patterns among normal image patches. Patches that do not share the characteristics of this normal population are detected and analyzed by oneclass support vector machine and 1-layer neural network. We apply the proposed method to a public breast cancer image set. Our results, in consultation with a senior pathologist, demonstrate that the proposed method outperforms existing methods. The obtained probability map could benefit the pathology practice by providing visualized verification data and potentially leads to a better understanding of data-driven diagnosis solutions.
Collapsing Superstring Conjecture

In the Shortest Common Superstring (SCS) problem, one is given a collection of strings, and needs to find a shortest string containing each of them as a substring. SCS admits $2\frac{11}{23}$-approximation in polynomial time (Mucha, SODA'13). While this algorithm and its analysis are technically involved, the 30 years old Greedy Conjecture claims that the trivial and efficient Greedy Algorithm gives a 2-approximation for SCS. We develop a graph-theoretic framework for studying approximation algorithms for SCS. The framework is reminiscent of the classical 2-approximation for Traveling Salesman: take two copies of an optimal solution, apply a trivial edge-collapsing procedure, and get an approximate solution. In this framework, we observe two surprising properties of SCS solutions, and we conjecture that they hold for all input instances. The first conjecture, that we call Collapsing Superstring conjecture, claims that there is an elementary way to transform any solution repeated twice into the same graph $G$. This conjecture would give an elementary 2-approximate algorithm for SCS. The second conjecture claims that not only the resulting graph $G$ is the same for all solutions, but that $G$ can be computed by an elementary greedy procedure called Greedy Hierarchical Algorithm. While the second conjecture clearly implies the first one, perhaps surprisingly we prove their equivalence. We support these equivalent conjectures by giving a proof for the special case where all input strings have length at most 3. We prove that the standard Greedy Conjecture implies Greedy Hierarchical Conjecture, while the latter is sufficient for an efficient greedy 2-approximate approximation of SCS. Except for its (conjectured) good approximation ratio, the Greedy Hierarchical Algorithm provably finds a 3.5-approximation.
EVOLUTION OF MEDIA COMPETENCES

This article presents issues related to the evolution of media and media competences, a review and analysis of selected historical, technological and educational conditions in the context of the development of digital technologies. A comparison is also made between digital, information and media competences, current development trends and future trends. The differences and requirements between qualified media users and qualified users of information technology are becoming less and less distinct. The 3 generations of Media education - 1.0, 2.0 and 3.0 were described. The main purpose of media education in the first phase of development, referred to as media education 1.0, was to develop not only critical thinking skills towards the media and media messages, but also - in a general sense - critical attitude and autonomy. Media 2.0 education can be discussed in connection with the dynamic development of the Internet and information and communication technologies, including social media, at the beginning of the 21st century. In the scТОntТПТc dТscoursО oП rОcОnt вОars, tСО concОpt oП „alРorТtСmТc culturО” Сas appОarОd, orТРТnallв defining a set of cultural artefacts that are software products, related to video games, and now describing the phenomenon in which the Big Data logic of large-scale machine learning algorithms change how culture is practiced, processed and understood (Gillespie, 2014). This stage of evolution of Media education could be identified as Media education 3.0. AI and VR and AR can accelerate teaching and learning processes through immersion, collaboration among users, realistic simulations and multi-channel communication. The topic is quite important and current in the context of changes in the education system at various levels and the challenges involved in preparing new programs.
Bringing the Blessing of Dimensionality to the Edge

In this work we present a novel approach and algorithms for equipping Artificial Intelligence systems with capabilities to become better over time. A distinctive feature of the approach is that, in the supervised setting, the approaches' computational complexity is sub-linear in the number of training samples. This makes it particularly attractive in applications in which the computational power and memory are limited. The approach is based on the concentration of measure effects and stochastic separation theorems. The algorithms are illustrated with examples.
Cellpackexplorer: Interactive model building for volumetric data of complex cells

In this paper, we describe cellPACKexplorer, a system designed to help developers of cellPACK find errors in and improve their algorithm. cellPACKexplorer focuses on visualizing the effects of cellPACK recipe parameters on the final packing output. We found that the developers have two different methods for understanding the output, numerical and visual, depending on their background. We designed cellPACKexplorer with a flexible interface to support both types of users. We evaluated our tool through case studies and questionnaires. Novice users were able to create cell models with cellPACK and explore the behavior of different parameters. Further, expert users discovered an error in the code and were able to locate the problem quickly with our new analysis tool. We conclude with a discussion of the implications of our findings in the wider visualization community.
When less is more in Neural Quality Estimation of Machine Translation. An industry case study

Quality estimation (QE) of machine translation (MT), the task of predicting the quality of an MT output without human references, is particularly suitable in dynamic translation workﬂows, where translations need to be assessed continuously with no speciﬁc reference provided. In this paper, we investigate sentence-level neural QE and its applicability in an industry use case. We assess six QE approaches, which we divide into two-phase and one-phase approaches, based on quality and cost. Our evaluation shows that while two-phase systems perform best in terms of the predicted QE scores, their computational costs suggest that alternatives should be considered for large-scale translation production.
User Experience-based Information Retrieval from Semistar Data Ontologies

The time necessary for the doubling of medical knowledge is rapidly decreasing. In such circumstances, it is of utmost importance for the information retrieval process to be rapid, convenient and straightforward. However, it often lacks at least one of these properties. Several obstacles prohibit domain experts extracting knowledge from their databases without involving the third party in the form of IT professionals. The main limitation is usually the complexity of querying languages and tools. This paper proposes the approach of using a keywords-containing natural language for querying the database and exploiting the system that could automatically translate such queries to already existing target language that has an efficient implementation upon the database. The querying process is based on data conforming to a Semistar data ontology that has proven to be a very easily perceptible data structure for domain experts. Over time, the system can learn from the user actions, thus making the translation more accurate and the querying – more straightforward.
Playing Iterated Rock-Paper-Scissors with an Evolutionary Algorithm

Evolutionary algorithms are capable offline optimizers, but are usually left out as a good option for a game-playing artificial intelligence. This study tests a genetic algorithm specifically developed to compete in a Rock-Paper-Scissors competition against the latest opponent from each type of algorithm. The challenge is big since the other players have already seen multiple revisions and are now at the top of the leaderboard. Even though the presented algorithm was not able to take the crown (it came second), the results are encouraging enough to think that against a bigger pool of opponents of varying difficulty it would be in the top tier of players since it was compared only to the best. This is no small feat since this is an example of how a carefully designed evolutionary algorithm can act as a rapid adaptive learner, rather than a slow offline optimizer.
Enriching Word Embeddings with Global Information and Testing on Highly Inflected Language

In this paper we evaluate our new approach based on the Continuous Bag-of-Words and Skip-gram models enriched with global context information on highly inflected Czech language and compare it with English results.As a source of information we use Wikipedia, where articles are organized in a hierarchy of categories.These categories provide useful topical information about each article.Both models are evaluated on standard word similarity and word analogy datasets.Proposed models outperform other word representation methods when similar size of training data is used.Model provide similar performance especially with methods trained on much larger datasets.
Application System for Identification of Surakarta Traditional Batik Images (SABATARA)

Surakarta Batik is a traditional cloth in Indonesia that has been designated as an intangible cultural heritage by the Ministry of Education and Culture. The Surakarta Batik Pattern has characteristics and has a story in each style. The method used affects the accuracy of each pattern in the Surakarta batik image. Image data used for training data are 100 image data with a size of 256 x 256 pixels, with test image data used as many as 20 image data. Improving the quality of the image using contrast stretching, the output is processed to separate objects with the background using adaptive thresholding. The obtained object is added by the canny process and calculated using the Gray Level Co-Occurrence Matrix to obtain the characteristics of each image. The characteristics used are four variables (energy, contrast, homogeneity, and correlation). The resulting variable is used as input to the classification using backpropagation. The test results obtained an accuracy rate of 95%, with an error rate of 0.05%.
Active Recall Networks for Multiperspectivity Learning through Shared Latent Space Optimization

Given that there are numerous amounts of unlabeled data available for usage in training neural networks, it is desirable to implement a neural network architecture and training paradigm to maximize the ability of the latent space representation. Through multiple perspectives of the latent space using adversarial learning and autoencoding, data requirements can be reduced, which improves learning ability across domains. The entire goal of the proposed work is not to train exhaustively, but to train with multiperspectivity. We propose a new neural network architecture called Active Recall Network (ARN) for learning with less labels by optimizing the latent space. This neural network architecture learns latent space features of unlabeled data by using a fusion framework of an autoencoder and a generative adversarial network. Variations in the latent space representations will be captured and modeled by generation, discrimination, and reconstruction strategies in the network using both unlabeled and labeled data. Performance evaluations conducted on the proposed ARN architectures with two popular datasets demonstrated promising results in terms of generative capabilities and latent space effectiveness. Through the multiple perspectives that are embedded in ARN, we envision that this architecture will be incredibly versatile in every application that requires learning with less labels.
Methodological and Structural Transformations of the Contemporary Science

И.В. Черникова, Д