Reviewerly/exHarmony huggingface dataset:
Neural Machine Translation Strategies for Generating Honorific-style Korean

Expression with honorifics is an important way of dressing up the language and showing politeness in Korean. For machine translation, generating honorifics is indispensable on the formal occasion when the target language is Korean. However, current Neural Machine Translation (NMT) models ignore generation of honorifics, which causes the limitation of the MT application on business occasion. In order to address the problem, this paper presents two strategies to improve Korean honorific generation ratio: 1) we introduce honorific fusion training (HFT) loss under the minimum risk training framework to guide the model to generate honorifics; 2) we introduce a data labeling (DL) method which tags the training corpus with distinctive labels without any modification to the model structure. Our experimental results show that the proposed two strategies can significantly improve the honorific generation ratio by 34.35% and 45.59%.
Behavioral Feature Analysis For Learner Affect Identification

The paper presents methodology for mapping of learner interaction patterns in virtual learning system to the affective state. A learning activity can lead the learner to confused or confident state of mind. This affective state of learner can be identified by interaction pattern analysis. The examination mode of learning system is designed with questionnaire based on Bloom's taxonomy cognitive levels. We present an approach for establishing relationship between cognitive level test performance of learner and affective state. Various non-intrusive interaction parameters captured during learning activity act as input features. We find that random forest algorithm provides very good accuracy in determining affective state.
A bayesian model of individual differences and flexibility in inductive reasoning for categorization of examples

Inductive reasoning is an important educational practice but can be difficult for teachers to support in the classroom due to the high level of preparation and classroom time needed to choose the teaching materials that challenge students' current views. Intelligent tutoring systems can potentially facilitate this work for teachers by supporting the automatic adaptation of examples based on a student model of the induction process. However, current models of inductive reasoning usually lack two main characteristics helpful to adaptive learning environments, individual differences of students and tracing of students' learning as they receive feedback. In this paper, we describe a model to predict and simulate inductive reasoning of students for a categorization task. Our approach uses a Bayesian model for describing the reasoning processes of students. This model allows us to predict students' choices in categorization questions by accounting for their feature biases. Using data gathered from 222 students categorizing three topics, we find that our model has a 75% accuracy, which is 10% greater than a baseline model. Our model is a contribution to learning analytics by enabling us to assign different bias profiles to individual students and tracking these profile changes over time through which we can gain a better understanding of students' learning processes. This model may be relevant for systematically analysing students' differences and evolution in inductive reasoning strategies while supporting the design of adaptive inductive learning environments.
Animal Tracking in Wildlife Footage with Quantum Particle Filter (QPF)

Animal behavior analysis in wildlife footage is a preoccupying domain in the society of wildlife biologist. In wildlife video, tracking of a particular animal in a herd is very challenging task due to its unpredictable motion, as well as analogous appearance with others. In order to deal with these issues, in the work we have proposed a maneuvering tracker that uses the notion of quantum measurement in Particle filter. The proposed Quantum Particle Filter (QPF) based tracking technique efficiently handles the abrupt motion of the animal in a video. Subjective and objective evaluation of the proposed method has been accomplished and compared with other state-of-the-art methods. Some wildlife videos from the publicly available benchmark data sets are used for experiments. The results shows the superiority of the proposed method over others.
Causal conflicts produce domino effects

Inconsistent beliefs call for revision—but which of them should individuals revise? A long-standing view is that they should make minimal changes that restore consistency. An alternative view is that their primary task is to explain how the inconsistency arose. Hence, they are likely to violate minimalism in two ways: they should infer more information than is strictly necessary to establish consistency and they should reject more information than is strictly necessary to establish consistency. Previous studies corroborated the first effect: reasoners use causal simulations to build explanations that resolve inconsistencies. Here, we show that the second effect is true too: they use causal simulations to reject more information than is strictly necessary to establish consistency. When they abandon a cause, the effects of the cause topple like dominos: Reasoners tend to deny the occurrence of each subsequent event in the chain. Four studies corroborated this prediction.
Performance Analysis of a Parallel Denoising Algorithm on Intel Xeon Computer System

This paper presents an experimental performance study of a parallel implementation of the Poissonian image restoration algorithm. Hybrid parallelization based on MPI and OpenMP standards is investigated. The implementation is tested for high-resolution radiographic images on a supercomputer using Intel Xeon processors as well as Intel Xeon Phi coprocessors. The experimental results show an essential improvement when running experiments for a variety of problem sizes and number of threads.
MD5 and ECC Encryption based framework for Cloud Computing Services

Cloud computing is emerging as a widely known paradigm in the field of computer science which initiates services like storing and accessing data over the internet instead of using computer's hard drives. There are also various services provided by cloud like Iaas, Paas and Saas. As the popularity of cloud increases, intruders have started to interrupt the process in order to access confidential file of various cloud users. There must exist a system to provide necessary features for security. To achieve security, cloud services uses various security principles like Confidentiality, access control, integrity, availability etc. In the current work all these principles are implemented in environment using algorithms like ECC to enhance the Confidentiality of data. In this scenario, MD5 maintains the Integrity of data at server side and access control is achieved by RBAC techniques. Hence the proposed architecture achieves the high level of security for the cloud environment.
Sentiment Polarity in Translation

Previous year, many researchers have been sentiment analysis on many focus languages. They analyzed and categorizing opinions expressed in a text. People express their opinions and feeling on social media as a daily routine. For sentiment analysis work, data plays an important role. Thus, social media become interested platform for opinion mining. On the other hand, low resource languages face less of sentiment resources (such as sentiment lexicon, corpus) than English language. It is needed to overcome language barriers and realize a sentiment platform capable of scoring in different languages when global opinion is need to decide something. In this paper, the expectations and limitations of machine translation in sentiment polarity task for Myanmar language is presented. We experiment with comments of particular news and general news that are expressed in social media news pages. Results show that sentiment transfer can be successful through human translation. This also demonstrates that translation from Myanmar to English has a significant effect on the preservation of sentiment by using translation engine. This happens primarily due to nature of Language but the results show that machine translation quality plays the important role in this work.
Artificial Intelligence Based Path Finding and Decision Making in First Person Shooting Game

Artificial Intelligence (AI) is one of the essential parts of gaming industry. In this paper, we have examined how Artificial intelligence technology is effectively used to solve the problems that arise while developing a game and integrate new features to the game. In this research paper, two algorithms are examined they are, path finding of the bots and bots decision making. Few AI techniques are discussed in this paper and how these techniques are used to provide solution to the problems that occur in the games. These techniques are also taken into consideration to provide new features into the computer games. This paper aims to discuss on the Finite State Machine and A* methodologies, which helps in the decision making of a bot and also to find the path towards the goal.
Simulation of Learning Logical Functions Using Single-Layer Perceptron

As a simplest neural network, the perceptron computes a linear combination of real-valued labeled training samples and predicts the classes for unclassified testing samples. If two different sets of samples can be separated by a straight line, they are called linearly separable. The perceptron can be considered as the binary classifier of unclassified samples based on the supervised machine learning approach. The learning algorithm for the perceptron takes a static value for the learning rate as an input, which can affect the efficiency of the learning process. This work attempts to implement logical operations OR, AND, NOR, and NAND using a single-layer perceptron algorithm. A modified perceptron algorithm is proposed which finds the most optimal value of learning for the learning process. The learning algorithm is provided with a range of learning rate values, and it picks the most suitable learning rate by calculating the number of iterations taken by each of these learning rates and choosing the one which takes the minimum number of the epoch.
The Implementation of Multiple Media in Improving Reading Comprehension Skill in the University Students

Abstract Most nations in the globe have recognized English as an international language. Consequently, English must be used in internationally both spoken and written communication. The people who live in the country which English to be the foreign language or even the second language has to learn it. In order, they can communicate with all people in the world globally. The teaching English as a foreign language in Indonesia has the objective to improve students’ capacity in acquiring the language itself. Among four language skills; reading one of the skill to be learned because by understanding the text, it can help the students to comprehend the knowledge of science and technology. Reading is also the “gate” to acquire science and technology. Through reading the text, the students will gain a lot of knowledge related to their field. Regarding the importance of the students in comprehension of the text, then, the objective of this paper is to give the contribution idea to improve the students reading comprehension skills for university students by using multiple media and creating the effective teaching-learning process in the higher education institutions. The final results of this study showed that the implementation of multiple media improve the students’ ability in comprehending the English text because the student feel enjoys the teaching and learning of reading. It is hoped that the use of various creative multiple media will use not only in reading classroom but also in other subjects of English learning.
Splittable Data Compression Codes

A structural property of a wide class of prefix free codes- splittability- is defined. It means that a codeword can be splitted into short chunks consisting of uniquely decodable encodings of integer pairs. We prove that two known families of data compression codes, namely Fibonacci and multi-delimiter codes, represent two structurally different classes of splittable codes. The splittability appears to be a powerful tool for establishing theoretical properties of data compression codes such as completeness, universality and asymptotic density. This is demonstrated for multi-delimiter codes, but also can be extended for other classes of splittable codes.
Data Driven Approach to Brahmi OCR Error Correction and Sinhala Meaning Generation from Brahmi Character Array

Brahmi is one of ancient scripting languages used in India, South and Central Asia. Brahmi inscriptions are the main source of getting information about ancient Sri Lanka. But manual translation process is very time consuming because Brahmi alphabet, word pattern, meaning and grammar are completely differing than the current Sinhala context. As these inscriptions have been discovered under the drip ledges of caves, rocks and slabs, it is a big challenge for Archaeologists to getting the data from these inscriptions and translating into Sinhala sentences because of various noises. This research paper is containing the after process of OCR character recognition phase, which proposed and implemented an efficient system which can take recognized Brahmi character array without word boundaries as input and convert it into meaningful Sinhala sentences by overcoming the OCR errors and word identification scoring method. Full system which consist of four modules is taking inputs of Brahmi inscription images, and this research module is the third module which takes the recognised Brahmi character array from the output of second module and mainly focused on word detection, overcome OCR errors using 3 error correction methods, Brahmi language model using 2 Bigram models and 1 Trigram model and translation.
A New Method of Cleaning Video from Impulse Noise

The paper proposes a new method of impulse noise filtering for video data processing. The method is based on the combined use of iterative processing and transformation of the result of median filtering based on the Lorentz distribution. The experimental part of the paper presents the results of comparing the quality of the proposed method with known analogs. Video distorted by impulse noise with pixel distortion probabilities from 1% to 99% inclusive was used for the simulation. Numerical assessment of the quality of cleaning video data from noise based on the peak signal-to-noise ratio (PSNR) showed that the proposed method shows the best result of processing in most considered cases, compared with the known analogs. The results obtained in the paper can be used in practical applications of digital video processing, for example, in systems of video surveillance, identification systems and control of industrial processes.
Immersive Search

Researchers in interactive information retrieval (IIR) have studied and refined 2D presentations of search results for years. Recent advances are bringing augmented reality (AR) and virtual reality (VR) to real-world systems, though the IIR community has done relatively little work to explore and understand aspects of 3D presentations of search results, effects of immersive environments, and the impacts of spatial cognition and different spatial arrangements of results displays in 3D. In the research proposed here, I outline my plan to use immerse environments to investigate how users' spatial cognition may influence the information retrieval process. Specifically, this work will observe how spatial arrangements of search results affect users' ability to find information in the post-query, visual search phase of the IIR process across quantitative and qualitative measures.
A Real-Time Network Traffic Identifier for Open 5G/B5G Networks via Prototype Analysis

Nowadays real-time traffic occupies lots of network resources, thus identification and analysis for this network traffic becomes urgent for operator and commercial company. With the identified traffic types, quality of experience (QoE) monitoring and optimization, user behavior analysis and network resource allocation are more beneficial for open 5G/Beyond 5G (5G/B5G) networks. Exiting studies usually adopt transport or application layer information to identify traffic, while we jointly consider them simultaneously to achieve general purpose identifier. Besides, we also analyze the flow-based features to reduce the corresponding complexity for low-complexity implementation. Based on anatomy of network traffic identification, we propose a traffic type identification framework for real-time traffic. In mainstream voice over Internet protocol (VoIP) call and video streaming services, the proposed method can achieve as much as 30% identification accuracy improvement and have more than 20% reduction in terms of the identification delay if compared with other conventional schemes.
Generating Heatmap for Unknown Documents towards Readability Measurement

The key idea behind this paper is to generate fixation heatmap of unknown documents to visualize and determine the focus areas in a document as a first step towards the readability measurement of the document. The data samples were collected by conducting experiment with nine participants reading 15 documents and the proposed method was to predict the fixation duration of each word in the documents. A Random Forest Regression model was used to predict the fixation duration per word and we achieved a mean regression score (R2) of 0.757 for all the documents.
Multi-task learning of deep neural networks for joint automatic speaker verification and spoofing detection

With the development of spoofing technologies, automatic speaker verification (ASV) systems have encountered serious challenges on security. In order to address this problem, many anti-spoofing countermeasures have been explored. There are two intuitive recipes to protect an ASV system from spoofing. The first one is to use a cascaded structure where spoofing detection is performed firstly and ASV is subsequently conducted only on the attempts which have passed the spoofing detection. The other one is to perform spoofing detection and ASV jointly. The discriminate reliably of the joint system has been proven to be more advantageous than cascaded systems with traditional methods, not only in accuracy, but also in convenience and computational efficiency. In this paper, we proposed a multi-task learning approach based on deep neural network to make a joint system of ASV and anti-spoofing. The performance of different acoustic features and structures of deep neural networks has been investigated on the ASVspoof 2017 version 2.0 dataset. The experimental results showed that the joint equal error rate (EER) of our approach was reduced by 0.55% compared to a joint system with Gaussian back-end fusion baseline.
Norm-Referenced Achievement Grading of Normal, Skewed, and Imperfectly Normal Distributions Based on Machine Learning versus Statistical Techniques

Grading is the process of interpreting learning performance to inform learners and instructors of current learning ability levels and necessary improvement. In norm referenced grading, the instructors conventionally use heuristics, statistic methods, or the mix of both. In the rise of artificial intelligence, we investigate if a machine learning technique is reasonably helpful for norm referenced grading in comparison to a statistical method and a heuristic method. We conduct all of the grading methods based on a normal-distribution data set, skewed-distribution data sets, and imperfectly-normal-distribution data sets. The results reveal that heuristic method and machine learning method yield similar grading results in several cases. Based on the real data sets, heuristic, machine learning, and statistical methods produce the best, moderate, and lowest grading qualities, respectively, in terms of Davies Boulden index.
Engineering Project-Based Learning Model Using Virtual Laboratory Mix Augmented Reality to Enhance Engineering and Innovation Skills

The purposes of this study were: (1) to develop Engineering Project-Based Learning Model Using Virtual Laboratory mix Augmented Reality to Enhance Engineering and Innovation Skills (2) to evaluate about the suitability of Engineering Project-Based Learning Model Using Virtual Laboratory mix Augmented Reality to Enhance Engineering and Innovation Skills by 3 education experts, 3 electrical communication engineering experts and 3 information technology and communication experts. The findings indicated that the Engineering Project-Based Learning Model Using Virtual Laboratory mix Augmented Reality to Enhance Engineering and Innovation Skills 4 Components: 1. Input with 5 steps: (1) Lesson objective (2) learner analysis (3) teacher analysis (4) content and (5) virtual laboratory, 2. Process have 5 steps: (1) problem identification and analysis (2) project definition (3) engineering design and problem-solving (4) implementation of engineering problem-solving (5) appropriate system development and control, 3. Output as an assessment that has (1) Engineering skills evaluation, (2) innovation evaluation, 4. Feedback that consists of engineering skills and innovation evaluation result. From the Engineering Project-Based Learning Model Using Virtual Laboratory mix Augmented Reality to Enhance Engineering and Innovation Skills, It was found that the result about the learning model is proper in the highest level $$ ({\bar{\text{X}}} = 4.36,\,{\text{S}}.{\text{D}}. = 0.19) $$. It’s shown that it can bring that model to use for the development of ability, Engineering and Innovation Skills.
FPGAs Logic Checking Method by Genetic Algorithms

Programmable logic integrated circuits are widely used in digital equipment, including equipment for critical applications. Therefore, the diagnosis of logic FPGA is an urgent task. Since the number of logic elements in modern FPGAs totals millions and the number of variables of individual logic elements totals eight the task of diagnosis is complicated. Existing methods for solving this problem often do not get the needed results in an acceptable time. The use of heuristic, including genetic, algorithms for diagnosing logic elements requires refinement to reflect these new factors in order to improve the built-in testing of FPGAs. GA for diagnosis of logic elements of FPGAs can significantly accelerate the diagnosis without losing its quality. Article investigates methods and tools for diagnosing the FPGA logic for highly reliable applications. The task is to perfect the methods for diagnosing the logic of the FPGA using new logic elements, based on genetic algorithms.
A note on the system <i>GRW</i> with the intensional contraction rule

Abstract In Ilić and Boričić (2014, Log. J. IGPL, 22, 673–695), the right-handed cut-free sequent calculus $GRW$ for the contraction-less relevant logic $RW$ is defined. In this paper, we show that the enlargement of the system $GRW$ with the structural rule of intensional contraction (WI) presents the sequent system for the principal relevant logic $R$ but the rule of cut cannot be eliminated in $GRW+$(WI).