Reviewerly/exHarmony huggingface dataset:
A Novel Method to Generate Key-Dependent S-Boxes with Identical Algebraic Properties

The s-box plays the vital role of creating confusion between the ciphertext and secret key in any cryptosystem, and is the only nonlinear component in many block ciphers. Dynamic s-boxes, as compared to static, improve entropy of the system, hence leading to better resistance against linear and differential attacks. It was shown in [2] that while incorporating dynamic s-boxes in cryptosystems is sufficiently secure, they do not keep non-linearity invariant. This work provides an algorithmic scheme to generate key-dependent dynamic $n\times n$ clone s-boxes having the same algebraic properties namely bijection, nonlinearity, the strict avalanche criterion (SAC), the output bits independence criterion (BIC) as of the initial seed s-box. The method is based on group action of symmetric group $S_n$ and a subgroup $S_{2^n}$ respectively on columns and rows of Boolean functions ($GF(2^n)\to GF(2)$) of s-box. Invariance of the bijection, nonlinearity, SAC, and BIC for the generated clone copies is proved. As illustration, examples are provided for $n=8$ and $n=4$ along with comparison of the algebraic properties of the clone and initial seed s-box. The proposed method is an extension of [3,4,5,6] which involved group action of $S_8$ only on columns of Boolean functions ($GF(2^8)\to GF(2)$ ) of s-box. For $n=4$, we have used an initial $4\times 4$ s-box constructed by Carlisle Adams and Stafford Tavares [7] to generated $(4!)^2$ clone copies. For $n=8$, it can be seen [3,4,5,6] that the number of clone copies that can be constructed by permuting the columns is $8!$. For each column permutation, the proposed method enables to generate $8!$ clone copies by permuting the rows.
Interactive Machine Comprehension with Information Seeking Agents

Existing machine reading comprehension (MRC) models do not scale effectively to real-world applications like web-level information retrieval and question answering (QA). We argue that this stems from the nature of MRC datasets: most of these are static environments wherein the supporting documents and all necessary information are fully observed. In this paper, we propose a simple method that reframes existing MRC datasets as interactive, partially observable environments. Specifically, we "occlude" the majority of a document's text and add context-sensitive commands that reveal "glimpses" of the hidden text to a model. We repurpose SQuAD and NewsQA as an initial case study, and then show how the interactive corpora can be used to train a model that seeks relevant information through sequential decision making. We believe that this setting can contribute in scaling models to web-level QA scenarios.
The Challenges of Optimizing Machine Translation for Low Resource Cross-Language Information Retrieval

When performing cross-language information retrieval (CLIR) for lower-resourced languages, a common approach is to retrieve over the output of machine translation (MT).However, there is no established guidance on how to optimize the resulting MT-IR system.In this paper, we examine the relationship between the performance of MT systems and both neural and term frequency-based IR models to identify how CLIR performance can be best predicted from MT quality.We explore performance at varying amounts of MT training data, byte pair encoding (BPE) merge operations, and across two IR collections and retrieval models.We find that the choice of IR collection can substantially affect the predictive power of MT tuning decisions and evaluation, potentially introducing dissociations between MT-only and overall CLIR performance.
Composite likelihood methods for histogram-valued random variables

Symbolic data analysis has been proposed as a technique for summarising large and complex datasets into a much smaller and tractable number of distributions -- such as random rectangles or histograms -- each describing a portion of the larger dataset. Recent work has developed likelihood-based methods that permit fitting models for the underlying data while only observing the distributional summaries. However, while powerful, when working with random histograms this approach rapidly becomes computationally intractable as the dimension of the underlying data increases. We introduce a composite-likelihood variation of this likelihood-based approach for the analysis of random histograms in $K$ dimensions, through the construction of lower-dimensional marginal histograms. The performance of this approach is examined through simulated and real data analysis of max-stable models for spatial extremes using millions of observed datapoints in more than $K=100$ dimensions. Large computational savings are available compared to existing model fitting approaches.
Combinatorial use of optical tracker, Gaussian Mixture PHD and group tracking for vehicle light tracking

We propose a combination of optical tracker, Probability Hypothesis Density (PHD) Filter and group tracking for tracking vehicle head lights and tail lights from an in vehicle, forward facing camera. We propose these systems are advantageous, because they can bridge several frames without outside detections and lead to more stable tracks than just using a traditional tracker, like a Kalman filter, on it's own. Additionally PHD does not need track-data association, but moves association uncertainty into the tracker, where it can be incorporated in covariance and noise calculations. Evaluation is performed with a closed source detector and a private data set. This evaluation proves the stability of the tracks and the trackers ability to bridge large amounts of time without external detections. This makes it a suitable choice for high difficulty situations that lead to the external detector missing light sources.
Mobile Video Action Recognition

Video action recognition, which is topical in computer vision and video analysis, aims to allocate a short video clip to a pre-defined category such as brushing hair or climbing stairs. Recent works focus on action recognition with deep neural networks that achieve state-of-the-art results in need of high-performance platforms. Despite the fast development of mobile computing, video action recognition on mobile devices has not been fully discussed. In this paper, we focus on the novel mobile video action recognition task, where only the computational capabilities of mobile devices are accessible. Instead of raw videos with huge storage, we choose to extract multiple modalities (including I-frames, motion vectors, and residuals) directly from compressed videos. By employing MobileNetV2 as backbone, we propose a novel Temporal Trilinear Pooling (TTP) module to fuse the multiple modalities for mobile video action recognition. In addition to motion vectors, we also provide a temporal fusion method to explicitly induce the temporal context. The efficiency test on a mobile device indicates that our model can perform mobile video action recognition at about 40FPS. The comparative results on two benchmarks show that our model outperforms existing action recognition methods in model size and time consuming, but with competitive accuracy.
인공지능 기반 음성로봇을 활용한 영어 말하기 학습 시스템 개발 방안

The purpose of this study is to provide a development plan for an English practice system through voice recognition robot technology based on...
Small Obstacle Avoidance Based on RGB-D Semantic Segmentation

This paper presents a novel obstacle avoidance system for road robots equipped with RGB-D sensor that captures scenes of its way forward. The purpose of the system is to have road robots move around autonomously and constantly without any collision even with small obstacles, which are often missed by existing solutions. For each input RGB-D image, the system uses a new two-stage semantic segmentation network followed by the morphological processing to generate the accurate semantic map containing road and obstacles. Based on the map, the local path planning is applied to avoid possible collision. Additionally, optical flow supervision and motion blurring augmented training scheme is applied to improve temporal consistency between adjacent frames and overcome the disturbance caused by camera shake. Various experiments are conducted to show that the proposed architecture obtains high performance both in indoor and outdoor scenarios.
Student Character Building on Metaphor in Raja Ali Haji’s and Robert Herrick’s Poems

The metaphor is figurative language that compares something with other things that has similar characteristics. Character building is the way to build mental of a person to be better especially in their personality aspect such as how to love his or her country, respect the difference, etc. The best way to conduct personality is an education by interesting educational personality in teaching learning. The descriptive qualitative research method is used to identify or describe the process and get deep understanding from those poems. After analyzing the data, it was found that 31 metaphors are used in these poems such as structural metaphor (4), orientational metaphor (17), and ontological metaphor (10), and also fifty data of character building from poem were found, including religious, tolerant, and patriotic characters.
Efficient Intelligent Framework for Selection of Initial Cluster Centers

At present majority of research is on cluster analysis which is based on information retrieval from data that portrays the objects and their association among them.When there is a talk on good cluster formation, then selection of an optimal cluster core or center is the necessary criteria.This is because an inefficient center may result in unpredicted outcomes.Hence, a sincere attempt had been made to offer few suggestions for discovering the near optimal cluster centers.We have looked at few versatile approaches of data clustering like K-Means, TLBOC, FEKM, FECA and MCKM which differs in their initial center selection procedure.They have been implemented on diverse data sets and their inter and intra cluster formation efficiency were tested using different validity indices.The clustering accuracy was also conducted using Rand index criteria.All the algorithms computational complexity was analyzed and finally their computation time was also recorded.As expected, mostly FECA and to some extend FEKM and MCKM confers better clustering results as compared to K-Means and TLBOC as the former ones manages to obtain near optimal cluster centers.More specifically, the accuracy percentage of FECA is higher than the other techniques however, it's computational complexity and running time is moderately higher.
Improved Storage for Efficient Private Information Retrieval

We consider the problem of private information retrieval from $N$ \emph{storage-constrained} databases. In this problem, a user wishes to retrieve a single message out of $M$ messages (of size $L$) without revealing any information about the identity of the message to individual databases. Each database stores $\mu ML$ symbols, i.e., a $\mu$ fraction of the entire library, where $\frac{1}{N} \leq \mu \leq 1$. Our goal is to characterize the optimal tradeoff curve for the storage cost (captured by $\mu$) and the normalized download cost ($D/L$). We show that the download cost can be reduced by employing a hybrid storage scheme that combines \emph{MDS coding} ideas with \emph{uncoded partial replication} ideas. When there is no coding, our scheme reduces to Attia-Kumar-Tandon storage scheme, which was initially introduced by Maddah-Ali-Niesen in the context of the caching problem, and when there is no uncoded partial replication, our scheme reduces to Banawan-Ulukus storage scheme; in general, our scheme outperforms both.
Principles for a Judgement Editor Based on Binary Decision Diagrams

We introduce the theoretical principles that underlie the design of a software tool which could be used by judges for making decisions about litigations and for writing judgements. The tool is based on Binary Decision Diagrams (BDD), which are graphical representations of truth-valued functions associated to propositional formulas. Given a type of litigation, the tool asks questions to the judge; each question is represented by a propositional atom. Their answers, true or false, allow to evaluate the truth value of the formula which encodes the overall recommendation of the software about the litigation. Our approach combines some sort of 'theoretical' or 'legal' reasoning dealing with the core of the litigation itself together with some sort of 'procedural' reasoning dealing with the protocol that has to be followed by the judge during the trial: some questions must necessarily be examined and sometimes in a specific order. That is why we consider extensions of BDD called Multi-BDD. They are BDD with multiple roots corresponding to the di erent specific issues that must necessarily be addressed by the judge during the trial. We illustrate our ideas on a case study dealing with French trade union elections which has been used throughout our project with the Cour de cassation. We also introduce the prototype developed during our project and a link with restricted access to try it out.
Sistem Informasi Penjualan Beras Berbasis Website

Abstract: The rapid development of the technology world requires technology users to keep up with its development. For this reason, new innovations have emerged in various aspects of life to facilitate human work. Sales are one aspect that is also growing rapidly. One new innovation that emerges is E-commerce or internet-based sales. E-commerce helps connect between sellers and buyers of different places through cyberspace. With the existence of E-commerce human work becomes more effective and efficient, because it can be accessed anytime and anywhere without limitation of time and region. Website is widely used for making E-commerce based sales systems. The website contains pages that are deepened in the form of information in the form of text, images, audio, video and animation that can be accessed by the public. The website-based sales system is very suitable for the sale of rice, because rice is one the staple foods of the Indonesian people and there are still many sellers who sell their rice products manually.&#x0D; Keywords: Innovation, E-commerce, website, Rice Sales
From A Shared Single Display Application to Shared Virtual Space Learning Application

Shared single display support the learning environment through multiple devices like mouse, keyboard or mobile phone. To what extend it is useful for university learning? We had identified some challenges in using shared single display. This paper reports the challenges in using a shared single display for learning activity in university. From the challenges, we explored a shared virtual space and the finding is reported in this paper. It is interesting to claim that shared virtual space is more promising in term of learning activities and development. The shared virtual space is easy to manage. However, creating sustainable and collaborative, scalable learning virtual space is a challenge and worth to explore in future.
Peramalan terhadap Forex dengan Metode ARIMA Studi Kasus GBP/USD

Forex or Foreign Exchange is trading a country's currency with another country's currency. The purpose of this study is basically to test the accuracy of ARIMA on the GBP/USD currency pair. In addition, this research is expected to provide the benefits of knowledge about forecasting using ARIMA. This study resulted in forecasting the GBP/USD currency pair within 1 month, per 6 months from January 2018 to June 2018 using the ARIMA method and R software. Data to be used are data taken from January 2013 to June 2018. For the the process will follow the process of the KDD (Knowledge Discovery in Database). The results obtained by the ARIMA model (3,2,1) as the best model to be applied for 1 month per 6 months on the GBP/USD currency pair because it has the lowest AIC value and the mean absolute percentage error is 3.16%.
Effect of Counselling Sessions on Achievement of Students in English at Senior Secondary Level

Languages play a vital role in human life. Language is a means of communication. It is the basis of human progress. It is the flesh and blood of human culture. Without language civilization could hardly even have begun and certainly could never have attained its higher forms. The teaching and learning of English in our schools are at very chaotic state. We have daily compulsory periods in English but our students passing out of schools perform very poorly in the subject and its practices. Counselling services play an important part in the development of the human being as an individual and education is the foundation for the development of human potential. Although the teaching-learning situation constitutes the heart of the educational process, yet counselling gives it a more meaningful purpose. The study here throws light upon the impact of counselling sessions on academic achievement of students in English at senior secondary level. The paper here reports the findings on effectiveness of counselling sessions which has been introduced in schools, KendriyaVidayalayas in particular on the achievement in English. The finding presented herewith is an outcome of an experimental research carried forward by the researchers on 72 senior secondary students. The study found out that the students those underwent with counselling sessions performed very well in comparison to the control group. It was also concluded that the effect of counseling sessions were sex as well as stream (science vs. commerce) neutral.
Fingerspelling recognition in the wild with iterative visual attention

Sign language recognition is a challenging gesture sequence recognition problem, characterized by quick and highly coarticulated motion. In this paper we focus on recognition of fingerspelling sequences in American Sign Language (ASL) videos collected in the wild, mainly from YouTube and Deaf social media. Most previous work on sign language recognition has focused on controlled settings where the data is recorded in a studio environment and the number of signers is limited. Our work aims to address the challenges of real-life data, reducing the need for detection or segmentation modules commonly used in this domain. We propose an end-to-end model based on an iterative attention mechanism, without explicit hand detection or segmentation. Our approach dynamically focuses on increasingly high-resolution regions of interest. It outperforms prior work by a large margin. We also introduce a newly collected data set of crowdsourced annotations of fingerspelling in the wild, and show that performance can be further improved with this additional data set.
Students’ English Language Learning Strategy and Implication to the Teaching and Learning Activity: A Study at Health Vocational Schools (SMK Kesehatan) in Ruteng at Flores

Different theorists proposed different definition of learning strategies (Wenden, 1987, O’Malley and Chamot, 1990, Oxford, 1990). All the definitions highlighted that learning strategies are the actions taken to make learning easier, faster, more enjoyable, more self-directed, more effective, and more transferrable. The study aims to find out how Health Vocational School students in Ruteng, Flores employ learning strategies, what strategy is mostly employed and the implications of learning strategy employed to language learning and teaching which cover cognitive strategy, meta cognitive strategy and social strategy. It’s mixed method research combining the use of quantitative and qualitative study. For the purpose of quantitative study, it was chosen 123 students by using the technique of cluster random sampling, meanwhile for the purpose of qualitative data, it was chosen 12 students and three English teachers by using purposive sampling. The research result shows that students of health vocational schools in Ruteng, Flores employ medium language learning strategy for the three learning strategies studied (Cognitive strategy employed by 90,2% (111) students, meta cognitive strategy employed by 86,2%(106) students, and social strategy employed by 86,2% (106) students. Regarding learning strategy mostly employed, the most frequent strategy is cognitive strategy which gains the mean 3.33. Meanwhile, the least strategy is social strategy and it reaches the mean of 3.26. Medium learning strategy for the three strategies studied, and cognitive strategy as the most frequent strategy employed has the implication to applying teaching method which is oriented to raise up students’ cognitive strategy, adopting regularly the meta cognitive instruction in the classroom to help students improve academic achievement, and providing the opportunity for students to practice speaking English out of the classroom.
A Study on Different Types of Scheduling Algorithm

To store the data in cloud computing, a scheduling algorithm is used traditionally. The main objective of this paper is to optimize and increase the data processing speed, minimize the time and increase the efficiency in the data storage process. In this paper, we have discussed about the different scheduling algorithms and the problems faced when storing the data.
Real-world Conversational AI for Hotel Bookings

In this paper, we present a real-world conversational AI system to search for and book hotels through text messaging. Our architecture consists of a frame-based dialogue management system, which calls machine learning models for intent classification, named entity recognition, and information retrieval subtasks. Our chatbot has been deployed on a commercial scale, handling tens of thousands of hotel searches every day. We describe the various opportunities and challenges of developing a chatbot in the travel industry.
An Eye for an Eye: Economics of Retaliation in Mining Pools

Currently, miners typically join mining pools to solve cryptographic puzzles together, and mining pools are in high competition. This has led to the development of several attack strategies such as block withholding (BWH) and fork after withholding (FAW) attacks that can weaken the health of PoW systems and but maximize mining pools' profits. In this paper, we present strategies called Adaptive Retaliation Strategies (ARS) to mitigate not only BWH attacks but also FAW attacks. In ARS, each pool cooperates with other pools in the normal situation, and adaptively executes either FAW or BWH attacks for the purpose of retaliation only when attacked. In addition, in order for rational pools to adopt ARS, ARS should strike to an adaptive balance between retaliation and selfishness because the pools consider their payoff even when they retaliate. We theoretically and numerically show that ARS would not only lead to the induction of a no-attack state among mining pools, but also achieve the adaptive balance between retaliation and selfishness.
Data Security Using Multi-bit LSB and Modified Vernam Cipher

Data security is one of the most important aspects of today's information era. Some methods are used to secure important data from hackers. The LSB is a steganographic algorithm that is often used to store data in the last bit. In order to improve the security, we combine steganography with cryptography enables. In this research LSB is modified using the multi-bit LSB model. Modifications are made to the bits of each character, the rotation by a certain amount can randomize the plaintext content before cryptographic algorithm, Vernam is performed. The bit on LSB can be inserted data as much as 1, 2, 3 or 4 - bit information. The calculation results of MSE and PSNR values indicate that the use of 1-bit LSB is superior to that of 2-, 3-, or 4-bit LSB.