Reviewerly/exHarmony huggingface dataset:
ECC over the ring F3d[ε],ε4=0 by using two methods

Let F3d is the finite field of order 3d with d be a positive integer, we consider A4:=F3d[ε]=F3d[X]/(X4) is a finite quotient ring, where ε4=0. In this paper, we will show an example of encryption and decryption. Firstly, we will present the elliptic curve over this ring. In addition, we study the algorithmic properties by proposing effective implementations for representing the elements and the group law. Precisely, we give a numerical example of cryptography (encryption and decryption) by using two methods with a secret key.
Advantages and disadvantages of blockchain-based electronic voting systems with homomorphic encryption

The work examines the phenomenon of electronic voting and advantages and disadvantages of such systems. Particular attention is paid to the electronic voting systems that use block chain technologies in their scheme. The purpose of the publication is to create a theoretical model of the electronic voting system, which will include all stages of the elections. In this work, a study and analysis of the existing electronic voting systems were carried out, the requirements for the creation of electronic voting systems were substantiated. Possible attacks on both conventional electronic voting systems and options using the block chain were given. The advantages of the work include theoretical and practical models, comparison and study of possible attacks on various types of electronic voting systems.
Size Based Characterization of Gold Nano Particles using Machine Learning Approach

In drug delivery, there is a need for precision in reporting particles parameters. Studies have shown that absorbency of drugs in the blood stream depends on the size of the nanoparticle. The shape and size of nanoparticles (NPs) matter the most, hence the distribution of NP depends on the size and shape of NPs. By synthesizing and characterizing the NPs, we are able to cluster and get the amount of a certain type of morphology and accurate size determination. Moreover, the size distribution of a particle plays a more important goal as it possesses an increase in the usability of a diagnostic and therapeutic tool in medicine. The shape and size distribution of NPs is important for the delivery of drugs and for the cure or treatment of several chronic diseases such as cancer. Hence it is important to get the accurate size distribution of NPs for better results. Gold nano particles (AuNPs) where measured manually by the use of transmission electron microscope, hence, in most cases human error could play part in terms of inaccurate measurements. The digital images of AnNPs contain noise, making it difficult to get accurate measurements using the transmission microscope. AuNPs were measured in terms of their width and length. This study focused on the characterization of AuNPs collected by the transmission electron microscope using machine learning approaches. Image preprocessing and processing techniques are used for extracting the features (length and width) of AuNPs. In this study, filtering techniques such as Gaussian blur, Median and Mean filtering techniques are employed for noise removal to increase the precision in estimating the size of NPs. Unsupervised machine learning algorithm such as K-means and Otsu are used to perform image segmentation of the filtered nano images for the accurate extraction of particles' features such as length and width. The size measurements obtained using the machine learning approaches are compared with the measurements taken by the transmission electron microscope (TEM) for error estimation in the size distributions of NPs. The results showed that machine learning approaches provided accurate measurements of most of the NPs as compared to TEM. Therefore, it is recommended that machine learning approaches can be used to estimate the size of NPs so that the shapes can be described better and classified during the synthesis process.
Equiprobable and attacked-square chess entropies by phases, levels of play and game outcomes

In any legal chess position, we define an attacked-square entropy [Formula: see text] for either Black or White pieces in terms of the square occupation probability [Formula: see text], where [Formula: see text] is the number of all possible movements to square [Formula: see text] (free or occupied by an opponent’s piece) and [Formula: see text] is the total mobility defined as the sum of all possible movements. Thus, each attacked square contributes to the entropy according to its received “firepower” concentration. A simpler nonsquare-dependant equiprobable entropy [Formula: see text] in terms of equal probabilities [Formula: see text] always yields [Formula: see text]. On average, the difference [Formula: see text] is very large in the Opening phase and [Formula: see text] decreases faster for lower ranked players after move 25. A major cause of the reduction of [Formula: see text] during a game is material loss, which is an irreversible process. By game outcome, gaps in average [Formula: see text] among winners, draws and losers are larger for Amateur players than for Elite players, both in the Middlegame and Endgame. Statistically, Elite players exhibit narrower dispersions in [Formula: see text]. Also, the entropy rates of the Elite level fluctuate much less than the entropies of other levels. Density of attacks in the four-square central zone is very high in the Opening, specially for Elite players.
CVAE-Based Complementary Story Generation Considering the Beginning and Ending

We studied the problem of the computer-based generation of a well-coherent story. In this study, we propose a model based on a conditioned variational autoencoder that takes the first and final sentences of the story as input and generates the story complementarily. One model concatenates sentences generated forward from the story’s first sentence as well as sentences generated backward from the final sentence at appropriate positions. The other model also considers information of the final sentence in generating sentences forward from the first sentence of the story. To evaluate the generated story, we used the story coherence evaluation model based on the general-purpose language model newly developed for this study, instead of the conventional evaluation metric that compares the generated story with the ground truth. We show that the proposed method can generate a more coherent story.
On the Modifications of a Digital Signature Algorithm with Secret Sharing

Cryptographic algorithms are vulnerable not only from attacks, but also from the issues raising from storing the private key. Once the private key is leaked, no matter how strong the algorithms are claimed to be, we are severely compromised and hence, lose the secrecy. One of the solutions to remedy this situation is to employ the secret sharing scheme. The basic idea of secret sharing is to split one private key value into several share values. Those share values are then stored into different places by different secret shareholders. The presence of only one share value cannot represent the whole secret value, and further, is unable to reconstruct the private key. In this study, we inserted a secret sharing scheme into a digital signature algorithm based on conic curve cryptography. We opted to perform the scheme on conic based algorithm because these curves are believed to have simpler computations than those of elliptic curves. We split the private key value into several share values, which are then utilized to generate signature values. The alteration on the signature value generation process (by adding a secret sharing scheme) does not alter the verification process taking place on the original algorithm (without the secret sharing scheme). We also employed an additional mechanism by using the solution of congruence system based on the Chinese Remainder Theorem (CRT) and Fermat’s Little Theorem. The whole process is expected to provide if not layers of security, a mechanism in which we can mitigate the possibility of losing secrecy.
Neural Ordinary Differential Equations for the Regression of Macroeconomics Data Under the Green Solow Model

We are interested in the regression of data to a parameterised system of differential equations formalising a dynamical system. We study the case of the Green Solow model, a neoclassical economics model for sustainable growth. Faced with the challenges posed by the coupling of the equations, the scarcity of data, and their auto-correlation, we devise several solutions. We present a baseline model and propose three models leveraging neural ordinary differential equations, a recently proposed machine learning model. We empirically and comparatively evaluate the performance of the four models. The results demonstrate the advantages of the proposed approach using neural ordinary differential equations. We conclude by discussing the generality of this knowledge- and data-driven approach to the analysis of dynamical systems.
Using multiple ASR hypotheses to boost i18n NLU performance.

Current voice assistants typically use the best hypothesis yielded by their Automatic Speech Recognition (ASR) module as input to their Natural Language Understanding (NLU) module, thereby losing helpful information that might be stored in lower-ranked ASR hypotheses. We explore the change in performance of NLU associated tasks when utilizing five-best ASR hypotheses when compared to status quo for two language datasets, German and Portuguese. To harvest information from the ASR five-best, we leverage extractive summarization and joint extractive-abstractive summarization models for Domain Classification (DC) experiments while using a sequence-to-sequence model with a pointer generator network for Intent Classification (IC) and Named Entity Recognition (NER) multi-task experiments. For the DC full test set, we observe significant improvements of up to 7.2% and 15.5% in micro-averaged F1 scores, for German and Portuguese, respectively. In cases where the best ASR hypothesis was not an exact match to the transcribed utterance (mismatched test set), we see improvements of up to 6.7% and 8.8% micro-averaged F1 scores, for German and Portuguese, respectively. For IC and NER multi-task experiments, when evaluating on the mismatched test set, we see improvements across all domains in German and in 17 out of 19 domains in Portuguese (improvements based on change in SeMER scores). Our results suggest that the use of multiple ASR hypotheses, as opposed to one, can lead to significant performance improvements in the DC task for these non-English datasets. In addition, it could lead to significant improvement in the performance of IC and NER tasks in cases where the ASR model makes mistakes.
Characterization and Prediction of Various Issue Types: A Case Study on the Apache Lucene System

Issue reports related to a software system provide an important source of information. However, an issue report has to go through various phases before it gets fixed. One such phase is issue-type assignment which is currently done manually. Manual issue-type assignment is not only time consuming but also error prone. Automated system can help in reducing time and error in issue-type assignment. In this paper, we work on characterization and prediction of different issue types. We perform characterization study with respect to three parameters: distribution, mean time to repair, and top terms present in various issue types. We compared several classic and ensemble machine learning classifiers with respect to accuracy and prediction time. The experimental results performed on the Apache Lucene project show that the machine learning-based issue-type classification approach is effective as it gave maximum accuracy of 67%. The classifiers multinomial Naïve Bayes and ensemble (hard voting) give the best results.
Sequential Recommendation via Temporal Self-Attention and Multi-Preference Learning

The sequential recommendation selects and recommends next items for users by modeling their historical interaction sequences, where the chronological order of interactions plays an important role. Most sequential recommendation methods only pay attention to the order information among the interactions and ignore the time intervals information, which leads to the limitations of capturing dynamic user interests. And previous work neglects diversity in order to improve recommendation accuracy. The model Temporal Self-Attention and Multi-Preference Learning (TSAMPL) is proposed to improve sequential recommendation, which learns dynamic and general user interests separately. The proposed temporal gate self-attention network is introduced to learn dynamic user interests, which takes both contextual information and temporal dynamics into account. To model general user interests, we employ a multi-preference matrix to learn users’ multiple types of preferences for improving recommendation diversity. Finally, the interest fusion module combines dynamic user interests (accuracy) and general user interests (diversity) adaptively. The experiments in sequential recommendation confirm our method is superior to all comparison methods, we also study the impact of each component in the model.
Modelling Volatility of Bitcoin Prices: Classical or Fractional Integrated GARCH Variants?

This paper tries to identify which class of GARCH variants best describe the volatility of bitcoin prices. The prices of bitcoin from August 7, 2015 to November 28, 2018 with 1210 daily observations were used. Five classical and four fractional integrated GARCH variants were estimated and based on the obtained AIC and SBIC, the four fractional integrated GARCH models fits better than the classical GARCH models. Results further showed that HYGARCH with Generalized error distribution (GED) tends to be the best fitted model for the bitcoin prices with better forecasting performance based on MAPFE and TI measures.
Theoretical and Experimental Study of Adaptive Control for Fixed-wing UAV Arrested Recovery on the USV

Shipborne recovery of small mixed-wing UAV is an important technology for UAV application in the sea, which have attracted much attention of researchers in recent years. Arrested recovery with arresting cable is one kind of effective recovery method of fixed-wing UAV. However, due to the severe limited recovery area, precision control of UAV is a key problem of UAV recovery and less tested work in the real surface environment are conducted. In our work, an adaptive control method combined with LPV model and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\alpha -\beta$</tex> filter for fixed-wing UAV arrested recovery on the USV is first proposed to achieve robust and high precision of the UAV during the recovery. Moreover, based on the independent shipborne recovery system and fixed-wing UAV, many flight experiments under different conditions in real environment are designed and conducted to verify the feasibility and performance of the system and methods. The results indicate that the UAV with the proposed method can satisfy the requirements of arrested recovery on the USV.
The Correlation between Copyright and Patent Forms of Industrial Design Protection

Most legal orders establish several forms of design protection that are not mutually exclusive. At the same time, the intersection of legal regimes gives rise to a number of practical and doctrinal problems related to the determination of the scope of the author’s and rightholder’s rights, the unfair behavior of participants in civil transactions, and the erosion of the institutional purpose of intellectual property objects. The main task of the study is to draw a meaningful border between the two forms of protection of industrial design objects. The paper elucidates the basic concepts of understanding an industrial design, the functions and features of the activity under consideration, the relationship with the category of "art," examines the formation and development of legal thought about the relationship between copyright and patent forms of protection of the results of artistic design. It is concluded that it is necessary to make proposals and recommendations to eliminate unfair practices in the exercise of the right to protect the results of creative activity under consideration.
Byte-Label Joint Attention Learning for Packet-grained Network Traffic Classification

Network traffic classification (TC) is to classify network traffic into a specific class which plays a fundamental role in terms of network measurement, network management, and so on. In this work, we focus on packet-grained traffic classification. We find that previous packet-grained methods based on the analogy between traffic packet and image or text are not sufficiently reasonable, leading to a sub-optimal performance on both accuracy and efficiency that still can be largely improved. In this paper, we devise a new method, called BLJAN, to jointly learn from byte sequence and labels for packet-grained traffic classification. BLJAN embeds the packet’s bytes and all labels into a joint embedding space to capture their implicit correlations with a dual attention mechanism. It finally builds a more powerful packet representation with an enhancement from label embeddings to achieve high classification accuracy and interpretability. Extensive experiments on two benchmark traffic classification tasks, including application identification and traffic characterization, with three real-world datasets, demonstrate that BLJAN can achieve high performance (96.2%, 96.7%, and 99.7% Macro F1-scores on three datasets) for packet-grained traffic classification, outperforming six representative state-of-the-art baselines in terms of both accuracy and detection speed.
Modelling and Simulation of Fuzzy-based Coordination of Trajectory Planning and Obstacle Avoiding for RRP-Typed SCARA Robots

In this article, a fuzzy-based solution of coordination between behaviors of trajectory planning and obstacle avoiding in a RRP-typed SCARA robot control is presented.The first idea of the proposed solution is to divide a robot's complex behavior into simpler parallel behaviors.The second key idea is a fuzzy-based coordination between these behaviors to make smooth robot motions without collision.The modelling and simulation on Matlab are executed to test the performance of the proposed solutions under basic circumstances.
Marketing virtual reality experiences … without the headset: watching, playing and fearing Anagram’s <i>Goliath</i>

If the immersive sector is to capture new audiences from different corners of the cultural landscape, then gateway promotional content is needed – that is, marketing that engages those new to immersive technologies. Is it possible, however, to promote the unique pleasures that Virtual Reality (VR) offers without putting people in headsets? Exploring this question, this article outlines research that experiments with new ways of marketing VR experiences based on applying theoretical concepts to a marketing campaign for Goliath, a 2021 VR documentary produced by Anagram. The campaign tested out Baekdal’s characterisation of two ‘moments’ of a VR experience: (1) a ‘macro moment’, which is experiencing VR yourself in a headset; and (2) a ‘micro moment’, which is presenting VR to people without a headset and whom prefer watching others in a VR environment rather participating in it themselves. This article argues that audiences new to VR are more likely to be engaged by promotion for VR if it presents a ‘micro moment’ of VR based on watching other people. We outline ways that the immersive sector can produce marketing for VR that better communicates not just what a VR experience is, but also what its audiences are expected to do.
ESTHER: A Recommendation System for Higher Education Programs

Automatic discovery of information in educational data has been broadening its horizons, opening new opportunities to its application. An open wide area to explore is the recommendation of undergraduate programs to high school students. However, traditional recommendation systems, based on collaborative filtering, require the existence of both a large number of items and users, which in this context are too small to guarantee reasonable levels of performance.
FA-GAN: High Resolution Face-Aging

Face-Aging aims at translating facial images from one age category to another while preserving source identity. When compared with existing Image generation/translation methods current Face-Aging methods work at lower resolution datasets. Hence, in this work we create a novel high resolution supervised Facial Aging dataset by classifying existing high quality facial datasets. Through qualitative and quantitative experiments on our newly created dataset as well as CACD dataset, we show superiority of our method in terms of quality and diversity when compared with existing methods. Quantitative improvements obtained are as high as 8% in terms of face-verification accuracy, and 2% (random samples), 62% (old samples) in terms of age-estimation accuracy, which becomes significant when put together in conjunction.
“I Think It Could Be Better”: Incorporating User Testing Into Learning Experience Design

While much of the literature regarding usability testing in libraries focuses on library websites, this article describes an approach that evaluates the usability of learning artifacts, such as learning objects and print materials, as well as learning experiences. In three case studies, we describe our approach to testing these materials and experiences, the results and improvements we made, and the lessons we learned that have informed our approach. We argue that librarians should incorporate usability testing into instruction, from testing learning objects in development to testing complex learning activities and print materials. This approach to usability testing and evaluation will give educators an evidence-based way to develop more effective learning materials and experiences, making them better for our learners.
Analysis of the use of rubber waste to improve the performance of the asphalt concrete mixture against tidal floods

Abstract The increasing rainfall can increase sea level, which increases the frequency of tidal flooding. The effects of tidal floods can emerge directly or indirectly, and the direct effects are the damage to people’s houses and infrastructure. Seawater puddles due to tidal floods have inundated road infrastructure. Meanwhile, humidity in asphalt is an essential factor affecting the durability of asphalt pavement. The asphalt concrete mixture requires a proper mixture to increase durability and life. The addition of used tire powder to the asphalt mixture can increase the asphalt pavement’s resistance to the effects of tidal flooding. The steps of this research include the preparation of materials, testing materials (coarse aggregate, fine aggregate, and asphalt), manufacturing of test objects with and without added materials rubber waste, the immersion of test specimens for 0 and 4 days, Marshall Stage 1 and Stage 2 testing, density testing, wheel tracking test. The results show that the effect of tidal floodwater immersion in the asphalt mixture decreases the strength and quality of the asphalt mixture, the skid resistance, which can be seen from the decreased stability and increased flow. The addition of crumb rubber to the asphalt mixture can reduce the decline in stability value, increase flow value, and better skid resistance results. The Wheel Tracking Machine test results show the effect of adding crumb rubber to the asphalt mixture to reduce the deformation speed of the asphalt mixture.