Reviewerly/exHarmony huggingface dataset:
Constraint-Based Gujarati Parser Using LPP

Integer linear programming has recently been used for decoding in a number of probabilistic models in order to enforce global constraints. However, in certain applications, such as non-projective dependency parsing and machine translation, the complete formulation of the decoding problem as an integer linear program renders solving intractable. We present an approach which solves the problem incrementally, thus we avoid creating intractable integer linear programs.
A dynamic programming-based approach for cloud instance type selection and optimisation

With the advantages of cloud computing gradually highlighted, users increasingly want to deploy their applications and services on the cloud to reduce costs and obtain high computing capacity. Nowadays, cloud providers (e.g., Amazon, Microsoft) at home and abroad provide a large amount of cloud instance types optimised to fit different use cases, such as compute optimised and memory optimised. Due to the potentially large quantity of cloud instance types in the public cloud market, it is often a challenge for users to select an optimal set of cloud instance types subject to limited resource capacity. In this paper, a dynamic programming-based approach is proposed for cloud instance type selection, which can provide optimal combination of cloud instance types to users. Experiments are performed based on real-world cloud information to evaluate the proposed method.
Process-oriented speaking assessment of primary English using AI chatbots: Possibilities and limitations

The present study aims to explore the possibilities and limitations of artificial intelligence (AI) chatbots in elementary schools in the context of process-oriented assessment, which is one of the main features of 2015 revised English curriculum. The researcher has developed an AI chatbot and implemented a speaking performance test using it for 3rd graders in an elementary school in Seoul. The results first show that a specific function of the AI chatbot which can track students utterances during the speaking test must be a strong point in process-oriented assessment. It provides teachers with very useful information about students speaking processes as well as performances, which could further be used to help improve students’ English proficiency. Second, the results of a survey indicate that students are generally fond of interacting with AI chatbots and that their interests in chatbots may motivate willingness to speak in English. However, the results also show some limitations of using AI chatbots in classroom. Despite the breakthrough in AI technology, AI chatbots still have technical problems that may severely harm feasibility of using chatbots in process-oriented assessment, e.g. low accuracy rate of voice recognition. There is also an issue of validity and reliability of the AI chatbot-based speaking test that should be endorsed by future research. It is expected that AI chatbots should be cautiously applied in process-oriented assessment towards making full use of the state-of-the-art AI technology.
Adversarial Machine Learning: An Interpretation Perspective

Recent years have witnessed the significant advances of machine learning in a wide spectrum of applications. However, machine learning models, especially deep neural networks, have been recently found to be vulnerable to carefully-crafted input called adversarial samples. The difference between normal and adversarial samples is almost imperceptible to human. Many work have been proposed to study adversarial attack and defense in different scenarios. An intriguing and crucial aspect among those work is to understand the essential cause of model vulnerability, which requires in-depth exploration of another concept in machine learning models, i.e., interpretability. Interpretable machine learning tries to extract human-understandable terms for the working mechanism of models, which also receives a lot of attention from both academia and industry. Recently, an increasing number of work start to incorporate interpretation into the exploration of adversarial robustness. Furthermore, we observe that many previous work of adversarial attacking, although did not mention it explicitly, can be regarded as natural extension of interpretation. In this paper, we review recent work on adversarial attack and defense, particularly, from the perspective of machine learning interpretation. We categorize interpretation into two types, according to whether it focuses on raw features or model components. For each type of interpretation, we elaborate on how it could be used in attacks, or defense against adversaries. After that, we briefly illustrate other possible correlations between the two domains. Finally, we discuss the challenges and future directions along tackling adversary issues with interpretation.
A Study on the Government's Countermeasures Against Cyber Attacks

The government of Republic of Korea has established and implemented comprehensive countermeasures whenever cyber attacks occur. However, the countermeasures up to now are limited to short-term measures focused on resolving individual issues. The purpose of this study is to suggest implications for the direction of the government's countermeasures against cyber threats with a focus on the National Cybersecurity Strategy. To this end, this study compares and analyzes the changes in Korea's cybersecurity measures that have been developed as the National Cybersecurity Measures, National Cybersecurity Posture Reinforcement Measures, and the National Cybersecurity Strategy. Next, in order to suggest implications for domestic cybersecurity measures, the directions and contents of the National Cyber Strategy of U.S. were compared. As a result, the U.S. has been aggressively considering the preemptive and retaliatory attacks against cyber threats, while Korea's strategy has been relatively defensive. The national cybersecurity strategy that defends against the recent quantitative and qualitative changes in cyber attacks also needs to prepare stronger countermeasures than before. In this context, the direction of U.S. cybersecurity strategy could be an implication.
Product design and creativity

Product design is an important aspect of the marketing mix and a crucial reflection of brand discourse and positioning. The tradition of conventional marketing goes together with a functionalist conception of product design, which prevailed for a long time. The notion of appropriation is crucial for the marketer and the designer. Designing and selling a functional and aesthetic high-performance product is not enough; to be successful it must conform to the consumer’s world. The conventional conception of marketing too often sees design as an “aesthetic touch” at the end of the process of making the product. Product design is part of a whole brand language with its specific signs, codes, and meanings, to be staged by managers. For managers to generate creativity and increase brand consistency awareness, a research program can combine different approaches. Analyzing online communities using both ethnography and semiotics together with focus groups, for instance, will facilitate consumer-inspired insights and innovations.
Penerapan Fuzzy Sugeno dalam Usaha Roti Ketawa

One of the food processing business that is much in demand by the community now is the laughing bread. The business that sells laughter bread products, Roti Ketawa Pelangi, is located in Jalan Sangnawaluh, the city of Pematangsiantar, North Sumatra province, where entrepreneurs in the Rainbow Laughter Bread are still having difficulty in determining the sales results for a week. This study aims to resolve the above problems by recommending a method. The method used in this study is the Sugeno fuzzy method. Data collection techniques conducted by researchers is a technique of direct interviews with an employee who is responsible for the business. In this study, the authors used 3 criteria including price, production, and sales. The results of this study are expected to provide input to business owners so they can count sales during the week.
On Irreducibility of the Stream Version of Asymmetric Binary Systems

The interval in ℕ composed of finite states of the stream version of asymmetric binary systems (ABS) is irreducible if it admits an irreducible finite-state Markov chain. We say that the stream version of ABS is irreducible if its interval is irreducible. Duda gave a necessary condition for the interval to be irreducible. For a probability vector (p,1-p), we assume that p is irrational. Then, we give a necessary and sufficient condition for the interval to be irreducible. The obtained conditions imply that, for a sufficiently small ε, if p∈(1/2,1/2+ε), then the stream version of ABS could not be practically irreducible.
Anomaly Detection via Neighbourhood Contrast

Relative scores such as Local Outlying Factor and mass ratio have been shown to be better scores than global scores in detecting anomalies. While this is true, our analysis reveals for the first time that these relative scores have a key shortcoming: anomalies have greatly different relative scores if they are located in different regions where the curvatures of the density surface are very different. As a result, the low-score anomalies could be ranked lower than some normal points. This revelation motivates (i) a new score called Neighbourhood Contrast (NC) which produces approximately the same high scores for all anomalies, regardless of varying curvatures of the density surface in different regions; and (ii) an anomaly detection method based on NC. Our experiments show that the proposed method which employs the new score significantly outperforms methods using the aforementioned relative scores on benchmark datasets.
Hierarchical Gradient Smoothing for Probability Estimation Trees

Decision trees are still seeing use in online, non-stationary and embedded contexts, as well as for interpretability. For applications like ranking and cost-sensitive classification, probability estimation trees (PETs) are used. These are built using smoothing or calibration techniques. Older smoothing techniques used counts local to a leaf node, but a few more recent techniques consider the broader context of a node when doing estimation. We apply a recent advanced smoothing method called Hierarchical Dirichlet Process (HDP) to PETs, and then propose a novel hierarchical smoothing approach called Hierarchical Gradient Smoothing (HGS) as an alternative. HGS smooths leaf nodes up to all the ancestors, instead of recursively smoothing to the parent used by HDP. HGS is made faster by efficiently optimizing the Leave-One-Out Cross-Validation (LOOCV) loss measure using gradient descent, instead of sampling used in HDP. An extensive set of experiments are conducted on 143 datasets showing that our HGS estimates are not only more accurate but also do so within a fraction of HDP time. Besides, HGS makes a single tree almost as good as a Random Forest with 10 trees. For applications that require more interpretability and efficiency, a single decision tree plus HGS is more preferred.
QR Code Digital Watermarking Algorithm Based on Two-Dimensional Logistic Chaotic Map

QR (Quick Response) codes, originally designed to facilitate tracking parts in automobile manufacturers, have been increasingly widely used in various fields, and gradually become an indispensable tool in people’s lives. However, with the development of network and multimedia technology, the pure QR code cannot effectively resist the two-dimensional code forgery and tampering attacks. Aiming at this shortcoming of QR code, this paper introduces digital watermarking technology into the protection of QR code image and proposes a digital watermark anti-counterfeiting algorithm based on QR code binary image. According to the characteristics of the QR code binary image, the algorithm combines the MD5 (Message-digest Algorithm 5) encryption algorithm and the two-dimensional logistic chaotic mapping algorithm to embed the watermark, which improves the security of the QR code based on the correct identification. Experimental results show that the watermarking algorithm has strong concealment, robustness, and large watermark capacity.
Home Atmosphere Visualization Based on Motion Sensing in Smart Living Room

Home atmosphere visualization method based on motion sensing is proposed to improve human atmosphere in a smart living. In the system, an atmosphere is calculated as a coordinate of 3-dimensional atmosfield, and is classified into two atmospheres based on motion sensor information. Living atmosphere is controlled by lighting, where the room lighting changes its color to encourage improvement of the atmosphere. Two types of presentment method are proposed and compared in TV program watching situation.
The Primal Sources of Threats: the Classification Model of Causes of Danger

The concept of threat is a central point of reflection on security. Numerous publications have already attempted to identify and classify known hazards. The fundamental problem that arises in the course of this task is a large number of dangers potentially threatening to a person. It is therefore justified to ask why such a large number of threats are posed and whether their main sources can be identified. In the text, the author analyzes the relationship between terms of threat and risk and discusses various ways of defining them. Adopting various perspectives for analyzing, considering and classifying dangers allows observe elements which constitute their primary source. The model for classifying primary causes of threats includes three basic sources of dangers: dependence, coexistence and change. The presented approach may be a basis for configuring and deploying tools for security subject diagnosis.
Pseudo Transfer Learning by Exploiting Monolingual Corpus: An Experiment on Roman Urdu Transliteration

This paper shares two things: an efficient experiment for “pseudo” transfer learning by using huge monolingual (mono-script) dataset in a sequence-to-sequence LSTM model; and application of proposed methodology to improve Roman Urdu transliteration. The research involves echoing monolingual dataset, such that in the pre-training phase, the input and output sequences are ditto, to learn the target language. This process gives target language based initialized weights to the LSTM model before training the network with the original parallel data. The method is beneficial for reducing the requirement of more training data or more computational resources because these are usually not available to many research groups. The experiment is performed for the character-based Romanized Urdu script to standard (i.e., modified Perso-Arabic) Urdu script transliteration. Initially, a sequence-to-sequence encoder-decoder model is trained (echoed) for 100 epochs on 306.9K distinct words in standard Urdu script. Then, the trained model (with the weights tuned by echoing) is used for learning transliteration. At this stage, the parallel corpus comprises 127K pairs of Roman Urdu and standard Urdu tokens. The results are quite impressive, the proposed methodology shows BLEU accuracy of 80.1% in 100 epochs of training parallel data (preceded by echoing the mono-script data for 100 epochs), whereas, the baseline model trained solely on parallel corpus yields ≈76% BLEU accuracy in 200 epochs.
Multi-Scale Decision Network with Feature Fusion and Weighting for Few-Shot Learning

Learning from limited labelled examples is key a research hotspot with excellent scenarios and potential applications. Currently, most of metric learning-based few-shot models still have the problem of low recognition accuracy. This is mainly because that they only use the top-layer abstract feature with semantic information, which ignores the low-layer features that are also critical for the few-shot recognition. Therefore, the extracted features do not have abundant representation ability, and it is difficult to recognize easily confusing objects. Moreover, they usually adopt a fixed distance function or train a comparable network to measure features. These methods lack adaptability, cannot sufficiently fuse features, which leads to weaken the fitting ability of the metric function. And the same or different classes of images are treated equally, which makes the metric function have no emphasis point during training. To address these issues, we propose an end-to-end, metric learning-based model in this paper, called multi-scale decision network with feature fusion and weighting for few-shot learning (MSDN). Considering the importance of the low-layer features, we exploit a convolutional network to extract each layer feature. Then, we exploit a relation network to learn a non-linear metric between the support set and the query set features of each layer and classify the test images via a voting decision. During feature concatenation, we design a non-linear feature fusion item to improve the way of concatenation, so that the relation network can have a stronger function fitting ability to learn the relation score. Meanwhile, we introduce the attention mechanism by calculating the cosine similarity between the support set and the query set features as their weight, which makes the relation network pay more attention to the same class of images. Our model achieves the state-of-the-art accuracy result on Omniglot and miniImageNet datasets compared with popular few-shot recognition models.
Implementation of Fuzzy Clustering Algorithms to Analyze Students Performance Using R-Tool

The special techniques like clustering and classification exist in data mining to handle any number of datasets that are available in the education field. The main use of data mining is to take out valuable information to create new knowledge in the field of education. The detection of low performers, improving the pass percentage and employment opportunities, is the main goal of every educational institution. In data mining, the well-known technique is to deal with disjoint and noisy data is clustering. This technique used for distance calculation between similar group objects and the different cluster centers is also found. In this paper, the implementation of fuzzy models like Fuzzy C-Means (FCM), Fuzzy Possibilistic C-Means (FPCM), Modified Fuzzy Possibilistic C-Means (MFPCM) and Fuzzy Possibilistic Product Partition C-Means (FPPPCM) clustering algorithms is used to measure the student’s levels and low performers identification through its size.
Organizing the informatics lesson using e-learning elements on the basis of a tablet computer

The emergence of new technical devices in the field of storage, processing and presentation of information at the present stage of development of society inevitably led to the need for their use in the educational process. An urgent methodological task is the development of lessons that fully use the didactic capabilities of tablet computers. The article presents the informatics lessons for 5 th and 6 th grades, which are carried out using tablet computers and relevant educational content.
GUIDED FEATURE MATCHING FOR MULTI-EPOCH HISTORICAL IMAGE BLOCKS POSE ESTIMATION

Abstract. Historical aerial imagery plays an important role in providing unique information about evolution of our landscapes. It possesses many positive qualities such as high spatial resolution, stereoscopic configuration and short time interval. Self-calibration reamains a main bottleneck for achieving the intrinsic value of historical imagery, as it involves certain underdeveloped research points such as detecting inter-epoch tie-points. In this research, we present a novel algorithm to detecting inter-epoch tie-points in historical images which do not rely on any auxiliary data. Using SIFT-detected keypoints we perform matching across epochs by interchangeably estimating and imposing that points follow two mathematical models: at first a 2D spatial similarity, then a 3D spatial similarity. We import GCPs to quantitatively evaluate our results with Digital Elevation Models (DEM) of differences (abbreviated as DoD) in absolute reference frame, and compare the results of our method with other 2 methods that use either the traditional SIFT or few virtual GCPs. The experiments show that far more correct inter-epoch tie-points can be extracted with our guided technique. Qualitative and quantitative results are reported.
The Viability of Image Analysis Measures of Visual Clutter in the AR UI Space as a Predictive Measures of User Performance

This study determines if existing image analysis measures of visual clutter are viable in the AR UI space, as they have only been established and tested for traditional displays. Image analysis measures of clutter were specifically chosen as they can be applied to complex and naturalistic images as is common to experience while using an AR UI. The end goal of this research is to use this study as the beginning of a clutter score algorithm, that is capable of predicting user performance for a given AR UI. In this experiment, twelve participants performed a visual search task of locating a target image in an array of images where some images were virtual and some were real. Participants completed this task under three different clutter levels (low, medium, high) against five different levels of virtual object percentage and two types of targets (real, virtual) with repetition. We measured task performance through response time. Our results show significant differences in response time between clutter levels. Participants consistently had more difficulty finding objects in more cluttered scenes, where clutter was determined through image analysis methods. Furthermore, response time was correlated to image analysis measures of clutter for combined (virtual and real) arrays but not for measures of clutter taken of the individual array components (virtual or real).
The Basic Acid Scientific Experiment Video and the Separation of Mixed-Based Information Technology

Abstract This research results in a video of scientific experiments worthy of use, resulting in a video of scientific experiments that is practical to use. The video is a video-based scientific experiment and a mixture of information technology. The development model in the study uses the Borg &amp; Gall development model which is adapted into a simple model. The results showed the validation of material experts with very decent categories, media experts with very decent categories, small group tests with very practical categories, and field trials with categories very practical.
A Comparative Study of Recommender Systems

The advent of modernization has led to a spurt in technological advancements. Consequently, the internet boom has resulted in the way we live our lives. The Internet has served as a platform for businesses which has yielded better results as compared to the traditional way of outdoor selling. Amazon, Netflix, Flipkart are such companies that have flourished online. These businesses use recommender systems to increase their selling based on customer preferences. These systems not only use a customer’s buying habits but also involve knowledge of reviews, ratings, correlation, similarity etc. involving several customers and items. Recommendation systems have been a part of research in their own respect. New robust algorithms have been developed over time aimed at improving system efficiency. This paper highlights the types of recommendation systems. Different systems follow different principles for providing user recommendations. An overview of such systems is showcased in the paper. The paper proposes a model based on a hybrid recommendation technique involving a combination of content and collaborative approaches.