Reviewerly/exHarmony huggingface dataset:
MIND: Model Independent Neural Decoder

Standard decoding approaches rely on model-based channel estimation methods to compensate for varying channel effects, which degrade in performance whenever there is a model mismatch. Recently proposed Deep learning based neural decoders address this problem by leveraging a model-free approach via gradient-based training. However, they require large amounts of data to retrain to achieve the desired adaptivity, which becomes intractable in practical systems. In this paper, we propose a new decoder: Model Independent Neural Decoder (MIND), which builds on the top of neural decoders and equips them with a fast adaptation capability to varying channels. This feature is achieved via the methodology of Model-Agnostic Meta-Learning (MAML). Here the decoder: (a) learns a "good" parameter initialization in the meta-training stage where the model is exposed to a set of archetypal channels and (b) updates the parameter with respect to the observed channel in the meta-testing phase using minimal adaptation data and pilot bits. Building on top of existing state-of-the-art neural Convolutional and Turbo decoders, MIND outperforms the static benchmarks by a large margin and shows minimal performance gap when compared to the neural (Convolutional or Turbo) decoders designed for that particular channel. In addition, MIND also shows strong learning capability for channels not exposed during the meta training phase.
On the number of autotopies of an $n$-ary qusigroup of order $4$

An algebraic system from a finite set $Σ$ of cardinality $k$ and an $n$-ary operation $f$ invertible in each argument is called an $n$-ary quasigroup of order $k$. An autotopy of an $n$-ary quasigroup $(Σ,f)$ is a collection $(θ_0,θ_1,...,θ_n)$ of $n+1$ permutations of $Σ$ such that $f(θ_1(x_1),...,θ_n(x_n))\equiv θ_0(f(x_1,\ldots,x_n))$. We show that every $n$-ary quasigroup of order $4$ has at least $2^{[n/2]+2}$ and not more than $6\cdot 4^n$ autotopies. We characterize the $n$-ary quasigroups of order $4$ with $2^{(n+3)/2}$, $2\cdot 4^n$, and $6\cdot 4^n$ autotopies.
THE EFFECT OF CONSTRUCTING AND ANALYZING MODEL IN ENHANCING TERTIARY EFL LEARNERS’ GRAMMAR AND WRITING ABILITY

This research aimed at examining the effect of Constructive and Analyzing Model on learners’ grammar mastery and writing ability at the university EFL context. The study also attempted to seek the light on how they perceived about the implementation of the model in learning grammar integrated with writing activities. A quasi-experimental with pretest–posttest non-equivalent control group design was employed, involving 49 university learners. Cloze-grammar tests and questionnaire were used to collect the data. Before administered, the instruments were tried out in a pilot class to establish validity and reliability. The experiment group was engaged in CAM activities following five major phases, such as text observation, form/function and discourse connection, grammar exploration, peer and reflective feedback, and comprehension check and clarification, meanwhile the control group received no treatment and was taught by using an ordinary method. The independent sample t-test was performed using SPSS 25 to investigate the difference of grammar mean score between these two groups in terms of S-V agreement and tenses. The results revealed that the Sig. (2-tailed) values of the experiment group and the control group were .015 and .586 respectively, and the significance value between the groups was .039. The findings showed that there was a significant difference on the grammar score between the experimental group and the control group. It revealed CAM could assist learners not only to understand grammar lesson more easily but also to make a use of their grammatical knowledge in writing more accurately. Furthermore, most of the participants perceived positively towards the implementation of CAM in learning grammar. Since the implementation of CAM is effective in grammar class, ELT practitioners are suggested to apply this model on other skills in different areas.&nbsp
Sistem Pakar Diagnosa Penyakit pada Sapi Berbasis Android

Handling reproductive disorders at the farm level is still lack due to the less information about the treatment of livestock which has a significant impact on decreasing of meat production. To reduce the impact of reproductive diseases, it is necessary to carry out early detection and treatment to prevent high losses and wider spread of disease. This article describes the application of an expert system that provides a means of consultation that uses an expert's reasoning process in solving complex problems regarding to cow reproductive health. In this study, the Tsukamoto Fuzzy method will be applied to help diagnosing the risk of disease in cattle based on six clinical symptoms. The result of this study is a mobile expert system that will conclude the risk level of endometriosis in cattle. This application is developed based on Android so that it is easy to use and it can be used by farmers to make a diagnosis independently. The result of this expert system testing indicates that this system can determine the level of endometritis risk of cow reproductive disease.�Keywords: expert system, android based, fuzzy logic, cow disease
Robust authenticated encryption scheme with multiple keys for ad hoc networks

Data security in a computing dynamic infrastructure without explicit user intervention is tough to achieve. A robust authenticated encryption scheme with multiple keys for ad hoc networks has been proposed. Real time attacks has been monitored and coped up using re-encryption algorithm. The effectiveness of the work has been validated by extensive simulations on various combination in terms of S-Boxes, key and data length. The proposed work is a collaboration of optimal selection of S-Boxes, key and data lengths with evaluation of heat dissipation. The work has been carried out to develop an optimized efficient key management technique to reduce the time available for hackers. To verify the effectiveness of proposed algorithm, the results have been compared with K. Xue et al. (2013) , Li et al. (2011) and S. K. Sood et al.’s protocol (2011)
IN-VEHICLE CAMERA IMAGES PREDICTION BY GENERATIVE ADVERSARIAL NETWORK

Moving object detection is one of the fundamental technologies necessary to realize autonomous driving.In this study, we propose the prediction of an in-vehicle camera image by Generative Adversarial Network (GAN).From the past images input to the system, it predicts the future images at the output.By predicting the motion of a moving object, it can predict the destination of the moving object.The proposed model can predict the motion of moving objects such as cars, bicycles, and pedestrians.
Movie trailer analysis based on editing features of movies

There are few diversity of movie trailers because the types of effects in a trailer are limited. Therefore, it is difficult to edit a trailer that caters to the different preferences of various users. To solve this problem, we define seven editing biases by images, audios and captions for the trailers, and we propose the user interface representing the result of analysis of the trailer.
Learning Relation Representations from Word Representations

Identifying the relations that connect words is an important step towards understanding human languages and is useful for various NLP tasks such as knowledge base completion and analogical reasoning. Simple unsupervised operators such as vector offset between two-word embeddings have shown to recover some specific relationships between those words, if any. Despite this, how to accurately learn generic relation representations from word representations remains unclear. We model relation representation as a supervised learning problem and learn parametrised operators that map pre-trained word embeddings to relation representations. We propose a method for learning relation representations using a feed-forward neural network that performs relation prediction. Our evaluations on two benchmark datasets reveal that the penultimate layer of the trained neural network-based relational predictor acts as a good representation for the relations between words.
Stability of decision trees and logistic regression

Decision trees and logistic regression are one of the most popular and well-known machine learning algorithms, frequently used to solve a variety of real-world problems. Stability of learning algorithms is a powerful tool to analyze their performance and sensitivity and subsequently allow researchers to draw reliable conclusions. The stability of these two algorithms has remained obscure. To that end, in this paper, we derive two stability notions for decision trees and logistic regression: hypothesis and pointwise hypothesis stability. Additionally, we derive these notions for L2-regularized logistic regression and confirm existing findings that it is uniformly stable. We show that the stability of decision trees depends on the number of leaves in the tree, i.e., its depth, while for logistic regression, it depends on the smallest eigenvalue of the Hessian matrix of the cross-entropy loss. We show that logistic regression is not a stable learning algorithm. We construct the upper bounds on the generalization error of all three algorithms. Moreover, we present a novel stability measuring framework that allows one to measure the aforementioned notions of stability. The measures are equivalent to estimates of expected loss differences at an input example and then leverage bootstrap sampling to yield statistically reliable estimates. Finally, we apply this framework to the three algorithms analyzed in this paper to confirm our theoretical findings and, in addition, we discuss the possibilities of developing new training techniques to optimize the stability of logistic regression, and hence decrease its generalization error.
Metacognitive Strategy for Training Analytic Reading of English Fictional/Literary Texts

The present article describes the pedagogical conditions, the principles of using metacognitive strategies.Its purpose is to reveal the relationships between the methods of interactive learning, analytical reading procedures and content metacognitivе strategies.Moreover, it substantiates the need for cooperation of the above-mentioned strategies in the process of forming reflexive-metacognitive competence of students and teachers.
PENGENALAN IRIS MATA MENGGUNAKAN METODE TEMPLATE MATCHING CORRELATION

Abstract - Biometrics is the study of automatic methods for recognizing humans based on one or more parts of the human body that are unique. One human characteristic that can be used is iris, iris features can be used as distinguishing characteristics with other individuals. The stage that the writer did to be able to recognize the iris pattern of someone's eye in a digital image was the pre-processing stage, the template saving stage and the matching stage. In this study the author applies the template matching method to store the image into a template image stored in the database and the algorithm correlation coefficient for the characteristic matching algorithm between template data and test data. The application is designed using the Matlab R2010a programming language. The results of testing 22 images obtained by the percentage of system success was 86.36%. Keywords - Iris, Template Matching, Correlation Coefficient
A Method to Calculate Correlation for Density Functional Theory on a Quantum Processor

An extension of the Variational Quantum Eigensolver (VQE) method is presented where a quantum computer generates an accurate exchange-correlation potential for a Density Functional Theory (DFT) simulation on classical hardware. The method enables efficient simulations of quantum systems by interweaving calculations on classical and quantum resources. DFT is implemented on classical hardware, which enables the efficient representation of and operation on quantum systems while being formally exact. The portion of the simulation operating on quantum hardware produces an accurate exchange-correlation potential but only requires relatively short depth quantum circuits.
Classifying Descriptions of Goods with Artificial Neural Networks

The present study aims to evaluate the performance of an artificial neural network in the classification of merchandise descriptions indicated in electronic bills, legal document used to record all commercial transactions in Brazil. For this, a significant sample of the actual descriptions will be used as well as a overlook about the performance of the neural network with a KNN and a GBM algorithms forecasting the category of the merchandise each description refers. This paper brings a method for classifying descriptions of goods with Artificial Neural Networks. The descriptions are small non structured texts, maximum of 120 characters, relating to goods traded in commercial transactions.
Construction Scheme of a Scalable Distributed Stream Processing Infrastructure Using Ray and Apache Kafka

The spread of various sensors and the development of cloud computing technologies en- able the accumulation and use of large numbers of live logs in ordinary homes. To operate a service that utilizes sensor data, it is difficult to install servers and storage in ordinary homes and to analyze the collected data from sensors. Those data are typically transmitted from sensors to a cloud and analyzed in the cloud. However, services that involve moving image analysis must transfer large amounts of data continuously and require high computing power for analysis. Hence, it is highly difficult to process them in real time in the cloud using a conventional stream data processing framework. In this research, we propose a construction scheme for a highly efficient distributed stream processing infrastructure that enables scalable processing of moving image recognition tasks according to the amount of data that are transmitted from sensors. We implement a prototype system of the proposed distributed stream processing infrastructure using Ray and Apache Kafka, which is a distributed messaging system, and we evaluate its performance. The experimental results demonstrate that the proposed distributed stream processing infrastructure is highly scalable.
A Three-Player GAN: Generating Hard Samples To Improve Classification Networks

We propose a Three-Player Generative Adversarial Network to improve classification networks. In addition to the game played between the discriminator and generator, a competition is introduced between the generator and the classifier. The generator's objective is to synthesize samples that are both realistic and hard to label for the classifier. Even though we make no assumptions on the type of augmentations to learn, we find that the model is able to synthesize realistically looking examples that are hard for the classification model. Furthermore, the classifier becomes more robust when trained on these difficult samples. The method is evaluated on a public dataset for traffic sign recognition.
ABC: A Cryptocurrency-Focused Threat Modeling Framework.

Cryptocurrencies are an emerging economic force, but there are concerns about their security. This is due, in part, to complex collusion cases and new threat vectors that could be missed by conventional security assessment strategies. To address these issues, we propose ABC, an Asset-Based Cryptocurrency-focused threat modeling framework capable of identifying such risks. ABC's key innovation is the use of collusion matrices. A collusion matrix forces a threat model to cover a large space of threat cases while simultaneously manages this process to prevent it from being overly complex. Moreover, ABC derives a system-specific threat categories that account for the financial aspects and the new asset types that cryptocurrencies introduce. We demonstrate that ABC is effective by conducting a user study and by presenting real-world use cases. The user study showed that around 71$\%$ of those who used ABC were able to identify financial security threats, as compared to only 13$\%$ of participants who used the popular framework STRIDE. The use cases further attest to the usefulness of ABC's tools for both cryptocurrency-based systems, as well as a cloud native security technology. This shows the potential of ABC as an effective security assessment technique for various types of large-scale distributed systems.
Web Based Inventory Management System In LotteMart Solo baru

LotteMart is an international company that started in South Korea and now the business has grown rapidly that the company now has 48 stores with two different type of store, wholesale and retail in Indonesia. The most common form of LotteMart is the wholesale type. In this project, the researcher used the newest type of store, the retail one, because that the article of goods that is not as much as the wholesale, so it is possible to sustain a prototype model of the wanted system. In this case of project, the researcher found that there is a possible development towards the non-embedded system, especially in the inventory management. The researcher concern about the inventory management post-procedure where the employee needs to find the missing goods that came from the difference between embedded system data and the result of the manual checked routine. The system that the researcher wanted to build is a system that capable to track down the goods starting from the moment it was received until the moment that it is sent to the display area also with the main feature to monitor the lifetime of every goods. In this project, the researcher using the web as the base system platform, to support the system with a real-time protection towards data loss and data manipulation. From the web framework itself, the researcher use django to provide more rapid development algorithm so that the system can be developed easily, but still in high security capability. The system works appropriately to supports store daily business process with 91.2% acceptance from the employee that related to the system.
Correction to: Meaning guides attention during scene viewing, even when it is irrelevant

A minor coding error slightly affected a few originally reported values.
Content Differences in Syntactic and Semantic Representations

Syntactic analysis plays an important role in semantic parsing, but the nature of this role remains a topic of ongoing debate. The debate has been constrained by the scarcity of empirical comparative studies between syntactic and semantic schemes, which hinders the development of parsing methods informed by the details of target schemes and constructions. We target this gap, and take Universal Dependencies (UD) and UCCA as a test case. After abstracting away from differences of convention or formalism, we find that most content divergences can be ascribed to: (1) UCCA's distinction between a Scene and a non-Scene; (2) UCCA's distinction between primary relations, secondary ones and participants; (3) different treatment of multi-word expressions, and (4) different treatment of inter-clause linkage. We further discuss the long tail of cases where the two schemes take markedly different approaches. Finally, we show that the proposed comparison methodology can be used for fine-grained evaluation of UCCA parsing, highlighting both challenges and potential sources for improvement. The substantial differences between the schemes suggest that semantic parsers are likely to benefit downstream text understanding applications beyond their syntactic counterparts.
A Study on the Role of Modern Education Apps on Students

Education and Development are two entities that cannot be separated from each other. Education is a process of learning, something that continues throughout our lives.  Considerable work has been accomplished by economists, academicians, social scientist to determine and measure the role and effect of education in the development process. India has the great heritage of knowledge since time immemorial. Knowledge has been flowing from one generation to the other since centuries. It is one of the oldest education systems in the world and is respected, rewarded in all capacities. However education is not restricted to scriptures and text books alone. Innovation and Technology have become predominant in every sector including education. People in India are also becoming tech friendly especially with the advent of Smart phone which has made education innovative and has enhanced the learning experience of every student. Thus it is rather compulsory for the education sector to adapt to the dynamic environment to keep pace with the competitive world.  The impact of both technology and digitization on students and teachers of today’s generation is a topic to be exploited and analyzed. The researchers in the paper have tried to analyze the effects and the roles of modern apps like LinkedIn, Unacademy, Coursera, TED etc. in the students’ academic exercise.
Conceptualization of WECARE: A Community-based Platform for Minimizing Consequences of Fall in Elderly People

The number of aged population is growing consistently, and is expected to triple by few next decades. One of the most threatening risk aged population have to face in their daily life is falling. Falling can pose serious injuries and consequences to elderly people, especially when they live alone or have cognitive dysfunction. If the elderly lives alone when an accident happens, it is more likely to take a long time until someone notices and provides rescue. Moreover, if the elderly has cognitive disorder such as Alzheimer’s disease, it is possible that he or she will not try to seek help. In this paper, an online platform called WECARE is proposed. WECARE aims to minimize the consequence of delayed rescue after a fall accident. The main advantage of the platform is the integration of community volunteers into the already widespread technologies such as fall detection systems and automatic doors. The benefits of volunteers could be a faster response after a fall accident and social connection provided to elderly people, especially the ones who live alone. This paper also describes how the idea was originated, modified, and improved through methodology. It also discusses advantages of the platform, limitations, as well as its future.
Three-bit fast error corrector for BCH codes on GPUs

Recent developments with General Purpose Graphical Processing units (GPGPU) within gaming and mobile platform has attracted researchers to contribute efficient methods to correct errors for Bose-Chaudhuri-Hocquenghem (BCH) codes. Three-bit error correction is widely used in Single Level Cell (SLC) flash memory arrays, and Optical Transport Networks (OTN). In this paper, we propose an optimized method for correcting three-bit errors in such system. Precisely, we optimize the kernel routine for key-equation solver to have better performance. We have devised an engine, on a GPGPU, that decodes code size of (n=1023, k=963, t=3) and compare it against the conventional inversion-less Berlekamp-Massey algorithm (iBMA). Then, we extend the same scheme to different code sizes, and the results have shown that there is a 20% increase in performance by reducing the latency incurred by the key equation solver routine.
An index theorem for split-step quantum walks

Split-step quantum walks are models of supersymmetric quantum walk, and thus their Witten indices can be defined. We prove that the Witten index of a split-step quantum walk coincides with the difference between the winding numbers of functions corresponding to the right-limit of coins and the left-limit of coins. As a corollary, we give an alternative derivation of the index formula for split-step quantum walks, which is recently obtained by Suzuki and Tanaka.