Reviewerly/exHarmony huggingface dataset:
AUGMENTED REALITY SEBAGAI MEDIA PEMBELAJARAN INOVATIF DI ERA REVOLUSI INDUSTRI 4.0

The main purpose of this paper is to uncover the results of a survey study regarding teacher opinions on the use of Augmented Reality (AR) in learning. For this purpose, marker-based cellular augmented reality applications have been developed and computer hardware is used as training material. In addition, teachers are given training for four days to get to know the basics of augmented reality technology, the software used, the environment to create marker-based AR objects. The data was collected through surveys and open-ended questions on teachers who are members of the English MGMP Kab. 50 cities. The tools and technology needed to develop AR and development experience are also shared. According to the survey results teachers are very enthusiastic about using augmented reality in their learning. The result of this article is the perception of the use of Augmented Reality technology for the development of instructional media by English teachers in the positive category.
A proposal for a recommender system of scientific relevance

The objective of this work is to present a proposal of a recommender system taking into account the scientific relevance of research groups and researchers by using indicators able to measure the productivity and impact of their publications. In the introduction, the related topics are presented, emphasizing the use of metadata, semantic textual similarity and bibliometric indicators. The methodology section exposes the steps for the design of the recommender system, considering the information gathering, building semantic knowledge, the use of Data and Text Mining, and the recommendations. Subsequently, a first implementation of the methodology used by a Mexican public university is presented. The results that have been obtained with Data and Text Mining techniques for the textual representation of the research groups are presented. Finally, some conclusions and future work are exposed.
A Novel Adjustable RDH Method for AMBTC-Compressed Codes Using One-to-Many Map

Most existing AMBTC (absolute moment block truncation coding)-based reversible data hiding (RDH) schemes cannot be decoded by traditional AMBTC decoders, because they change the coding structure of the AMBTC compressed code stream, which is unidentified for decoders. Even if some AMBTC-based RDH methods can be decoded by traditional AMBTC decoders, but the obtained payload is low. To this end, in this paper, a high-capacity and decodable AMBTC-based RDH scheme is presented. It is well observed that an m × n-sized bitmap has 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m×n</sup> combinations of `1' and `0'. However, all the bitmaps of an image occupy only a small part of all the combinations, and a large part are not used in this image. Motivated by this observation, we propose a one-to-many map between the used and unused combinations, in which one used bitmap can be mapped to multiple unused combinations, achieving high payloads (e.g., log <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> 25 bits for one bitmap of Lena), and more importantly, completely reserving the coding structure of the AMBTC-compressed code. Unlike existing decodable AMBTC-based RDH methods only capable of achieving a fixed payload for one test image, our method can adjust adaptively payloads according to the required visual quality by introducing a predefined threshold. The experimental results also demonstrate that our proposed scheme can largely increase the payload on the basis of maintaining good visual quality.
Neural Machine Translation with Joint Representation

Though early successes of Statistical Machine Translation (SMT) systems are attributed in part to the explicit modelling of the interaction between any two source and target units, e.g., alignment, the recent Neural Machine Translation (NMT) systems resort to the attention which partially encodes the interaction for efficiency. In this paper, we employ Joint Representation that fully accounts for each possible interaction. We sidestep the inefficiency issue by refining representations with the proposed efficient attention operation. The resulting Reformer models offer a new Sequence-to-Sequence modelling paradigm besides the Encoder-Decoder framework and outperform the Transformer baseline in either the small scale IWSLT14 German-English, English-German and IWSLT15 Vietnamese-English or the large scale NIST12 Chinese-English translation tasks by about 1 BLEU point. We also propose a systematic model scaling approach, allowing the Reformer model to beat the state-of-the-art Transformer in IWSLT14 German-English and NIST12 Chinese-English with about 50% fewer parameters. The code is publicly available at https://github.com/lyy1994/reformer.
Dataset Analysis and Augmentation for Emoji-Sensitive Irony Detection

Irony detection is an important task with applications in identification of online abuse and harassment.With the ubiquitous use of nonverbal cues such as emojis in social media, in this work we study the role of these structures in irony detection.Since the existing irony detection datasets have <10% ironic tweets with emoji, classifiers trained on them are insensitive to emojis.We propose an automated pipeline for creating a more balanced dataset.
Indirect Stochastic Gradient Quantization and Its Application in Distributed Deep Learning

Transmitting the gradients or model parameters is a critical bottleneck in distributed training of large models. To mitigate this issue, we propose an indirect quantization and compression of stochastic gradients (SG) via factorization. The gist of the idea is that, in contrast to the direct compression methods, we focus on the factors in SGs, i.e., the forward and backward signals in the backpropagation algorithm. We observe that these factors are correlated and generally sparse in most deep models. This gives rise to rethinking of the approaches for quantization and compression of gradients with the ultimate goal of minimizing the error in the final computed gradients subject to the desired communication constraints. We have proposed and theoretically analyzed different indirect SG quantization (ISGQ) methods. The proposed ISGQ reduces the reconstruction error in SGs compared to the direct quantization methods with the same number of quantization bits. Moreover, it can achieve compression gains of more than 100, while the existing traditional quantization schemes can achieve compression ratio of at most 32 (quantizing to 1 bit). Further, for a fixed total batch-size, the required transmission bit-rate per worker decreases in ISGQ as the number of workers increases.
Monitoring Based Security Approach for Cloud Computing

Cloud Service owner manages and maintains a variety of services for the end-users and enterprises.To provide security to user data, many security methods can be applied.The purpose of this paper is to design Monitor based scheme that provides the security to user data.The main components associated with the scheme are Client, Monitor and Cloud Service provider.The client performs various operations on the file he wants to store into the cloud.Few of the actions are the division of file into blocks, encoding the file, generation of hashing on the file and application of signature on the data.The monitor does the verification part on behalf of the client, and also responsible for matching the signature on the data files if both the signature matches then declare that integrity of the data is maintained.Cloud server just stores the data sent by the client and provide the data to the client on the request.User can guide the monitor of the monitoring process when to check the integrity of data.So the whole scheme is to develop a monitoring method, which has many security features like privacy maintenance, data integrity maintenance, and data privacy.The approach makes use of cryptography algorithms to achieve the desired results.In this approach, an efficient monitor plays a crucial role in securing the cloud environment.
Texts with Various Levels of Hardness, Reading Comprehension and Reading Motivation: I+1 Versus I-1

Considering the vital role of comprehensible input, this study attempted to compare the effects of input with various difficulty levels on Iranian EFL learners’ reading comprehension and reading motivation. To fulfil this objective, 54 Iranian pre-intermediate EFL learners were selected from two intact classes (n = 27 each). The selected participants were randomly assigned to two equal groups, namely “i+1” (n=27) and “i-1” group (n=27). Then, the groups were pretested by a researcher-made reading comprehension test. After carrying out the pre-test, the treatment (i.e., extensive reading at different levels of difficulty) was practiced on the both groups. The participants in “i+1” group received reading passages beyond the current level, on the other hand, the “i-1” group received those reading passages which were below their current level. After the instruction ended, a modified version of pre-test was conducted as posttest to determine the impacts of the treatment on the students’ reading comprehension. The obtained results indicated that there was a significant difference between the post-tests of “i+1” and “i-1” groups. The findings showed that the “i+1” group significantly outperformed the “i-1” group (p &lt; .05) on the post-test. Moreover, the findings indicated that “i+1” group’s motivation increased after the treatment. The implications of the study suggest that interactive type of input is beneficial to develop students’ language skills.&#x0D; Keywords: Comprehensible Input; Extensive reading; Foreign language reading motivation; Input; Reading comprehension; Text difficulty level
Augmented reality using features accelerated segment test for property catalogue

Promotional media used in the marketing of housing using catalogs that display 2D images of houses from one side of the house make potential customers unable to imagine the design of all parts of the house. Augmented Reality can be used as an interactive marketing media so that it can be used to display homes in 3D so that they appear more real from all sides so that prospective customers can consider the type of house to be chosen. Development of this application using the Multimedia Development Life Cycle. Application development uses the FAST algorithm as detection of home catalog markers to define how well images can be detected and tracked. The FAST algorithm will calculate every pixel on the target image in determining the corner when scanning the home catalog then it will produce a 3D object home to see the real shape design of the house.
Structure Learning for Approximate Solution of Many-Player Games

Games with many players are difficult to solve or even specify without adopting structural assumptions that enable representation in compact form. Such structure is generally not given and will not hold exactly for particular games of interest. We introduce an iterative structure-learning approach to search for approximate solutions of many-player games, assuming only black-box simulation access to noisy payoff samples. Our first algorithm, K-Roles, exploits symmetry by learning a role assignment for players of the game through unsupervised learning (clustering) methods. Our second algorithm, G3L, seeks sparsity by greedy search over local interactions to learn a graphical game model. Both algorithms use supervised learning (regression) to fit payoff values to the learned structures, in compact representations that facilitate equilibrium calculation. We experimentally demonstrate the efficacy of both methods in reaching quality solutions and uncovering hidden structure, on both perfectly and approximately structured game instances.
Personalized Recommender System Using A Social Network Based Collaborative Filtering Technique

Point of interest (POI) recommendation has attracted a lot of research attention by combining user ratings and POIs to find the similarity of users to help them to locate an enjoyable place. However, social networks, which have become a part of modern lifestyle, contain much information about the relationship between users and POIs, such as checkin activity. POI recommendations should consider the significant features of check-in data from location-based social networks (LBSNs) for more precise results for POI recommendations. This paper presents a personalized recommender system using a social network based collaborative filtering technique that recommends the top-n POIs to the user. The proposed method calculates similarity based on user ratings with a collaborative filtering technique and user check-in activity on a social network to make personalized recommendations. We used a real-world dataset from Foursquare to test our method. The results from experiments demonstrate that using social network check-in activity combined with a collaborative filtering method can increase the performance of the recommender system in the real word.
Numerical Simulation of an InP Photonic Integrated Cross-Connect for Deep Neural Networks on Chip

We propose a novel photonic accelerator architecture based on a broadcast-and-weight approach for a deep neural network through a photonic integrated cross-connect. The single neuron and the complete neural network operation are numerically simulated. The weight calibration and weighted addition are reproduced and demonstrated to behave as in the experimental measurements. A dynamic range higher than 25 dB is predicted, in line with the measurements. The weighted addition operation is also simulated and analyzed as a function of the optical crosstalk and the number of input colors involved. In particular, while an increase in optical crosstalk negatively influences the simulated error, a greater number of channels results in better performance. The iris flower classification problem is solved by implementing the weight matrix of a trained three-layer deep neural network. The performance of the corresponding photonic implementation is numerically investigated by tuning the optical crosstalk and waveguide loss, in order to anticipate energy consumption per operation. The analysis of the prediction error as a function of the optical crosstalk per layer suggests that the first layer is essential to the final accuracy. The ultimate accuracy shows a quasi-linear dependence between the prediction accuracy and the errors per layer for a normalized root mean square error lower than 0.09, suggesting that there is a maximum level of error permitted at the first layer for guaranteeing a final accuracy higher than 89%. However, it is still possible to find good local minima even for an error higher than 0.09, due to the stochastic nature of the network we are analyzing. Lower levels of path losses allow for half the power consumption at the matrix multiplication unit, for the same error level, offering opportunities for further improved performance. The good agreement between the simulations and the experiments offers a solid base for studying the scalability of this kind of network.
Through the eyes of a poet

With the increasing popularity of portable devices with cameras (e.g., smartphones and tablets) and ubiquitous Internet connectivity, travelers can share their instant experience during the travel by posting photos they took to social media platforms. In this paper, we present a new image-driven poetry recommender system that takes a traveler's photo as input and recommends classical poems that can enrich the photo with aesthetically pleasing quotes from the poems. Three critical challenges exist to solve this new problem: i) how to extract the implicit artistic conception embedded in both poems and images? ii) How to identify the salient objects in the image without knowing the creator's intent? iii) How to accommodate the diverse user perceptions of the image and make a diversified poetry recommendation? The proposed iPoemRec system jointly addresses the above challenges by developing heterogeneous information network and neural embedding techniques. Evaluation results from real-world datasets and a user study demonstrate that our system can recommend highly relevant classical poems for a given photo and receive significantly higher user ratings compared to the state-of-the-art baselines.
Verifying solutions to LWE with implications for concrete security

<p style='text-indent:20px;'>A key step in Regev's (2009) reduction of the Discrete Gaussian Sampling (DGS) problem to that of solving the Learning With Errors (LWE) problem is a statistical test required for verifying possible solutions to the LWE problem. We derive a lower bound on the success probability leading to an upper bound on the tightness gap of the reduction. The success probability depends on the rejection threshold <inline-formula><tex-math id="M1">\begin{document}$ t $\end{document}</tex-math></inline-formula> of the statistical test. Using a particular value of <inline-formula><tex-math id="M2">\begin{document}$ t $\end{document}</tex-math></inline-formula>, Regev showed that asymptotically, the success probability of the test is exponentially close to one for all values of the LWE error <inline-formula><tex-math id="M3">\begin{document}$ \alpha\in(0,1) $\end{document}</tex-math></inline-formula>. From the concrete analysis point of view, the value of the rejection threshold used by Regev is sub-optimal. It leads to considering the lattice dimension to be as high as 400000 to obtain somewhat meaningful tightness gap. We show that by using a different value of the rejection threshold and considering <inline-formula><tex-math id="M4">\begin{document}$ \alpha $\end{document}</tex-math></inline-formula> to be at most <inline-formula><tex-math id="M5">\begin{document}$ 1/\sqrt{n} $\end{document}</tex-math></inline-formula> results in the success probability going to 1 for small values of the lattice dimension. Consequently, our work shows that it may be required to modify values of parameters used in an asymptotic analysis to obtain much improved and meaningful concrete security.
Simplicial Complexes are Game Complexes

Strong placement games (SP-games) are a class of combinatorial games whose structure allows one to describe the game via simplicial complexes. A natural question is whether well-known parameters of combinatorial games, such as "game value", appear as invariants of the simplicial complexes. This paper is the first step in that direction. We show that every simplicial complex encodes a certain type of SP-game (called an "invariant SP-game") whose ruleset is independent of the board it is played on. We also show that in the class of SP-games isomorphic simplicial complexes correspond to isomorphic game trees, and hence equal game values. We also study a subclass of SP-games corresponding to flag complexes, showing that there is always a game whose corresponding complex is a flag complex no matter which board it is played on.
User and Topic Hybrid Context Embedding for Finance-Related Text Data Mining

As large amounts of finance-related microblogs, posted by both professional and amateur investors, are mingling in online social networks that could bring impact to real financial markets, it's an essential task to analyze financial texts posted by different users. Besides that, as barely nobody could be an all-round expert in various financial products such as stocks, futures and bonds, topic preferences of different users should also be considered in these mining tasks. That is to say, financial text contents should be analyzed in both user and topic level. In recent advances of deep learning technology, learning the "embedding" representation of contents is a state-of-art method in financial text mining over hand-crafted feature extraction techniques. Along this line, in this paper we propose a hybrid embedding method that both the "embedding" representation of users and the "embedding" representation of financial topics are learned from the posted finance-related contents in online social networks. Following the commonly used methods in embedding-related researches, we apply extensive experiments with real-world datasets to show the effectiveness of our hybrid user and topic embedding (UTE) approach, in both intrinsic and extrinsic ways. It's shown that our approach can intrinsically distinguish social network users with their learned embeddings, so they can be grouped into human-explainable clusters. Moreover, we also propose a case study on sentiment analysis by applying our user and topic hybrid embedding with a deep contextual neural network architecture. The results prove that our approach outperforms other baseline methods in financial-related sentiment analysis, and hopefully for other downstream text mining tasks as well.
Dual-Domain-Based Adversarial Defense With Conditional VAE and Bayesian Network

Adversarial examples can be imperceptible to human eyes but can easily fool deep models. Such intrigue property has raised security issues for real-world industrial deep learning systems. To combat those malicious attacks, a novel defense strategy has been proposed based on the conditional variational autoencoder (CVAE) and Bayesian network (BN). The main contribution lies in the provided systematic dual-domain-based defense framework, which covers three modules named detection, diagnosis, and recovery. Specifically, the CVAE is first introduced for latent- and residual-domain generation. Subsequently, a composite and hierarchical BN detector is proposed to conduct the adversary detection through feature validation and output justification. Afterwards, a diagnosis strategy has been constructed for residual domain and different attacks can be evaluated in the unified framework. Finally, a two-step recovery mechanism is established on the CVAE that can effectively restore the feature representations and the network predictions from various adversaries. The feasibility of the entire defense diagram has been extensively demonstrated on three real-world recognition problems.
“Visual Affluence” in Social Photography: Applicability of Image Segmentation as a Visually Oriented Approach to Study Instagram Hashtags

The aim of the study is to examine the applicability of image segmentation—identification of objects/regions by partitioning images—to examine online social photography. We argue that the need for a meaning-independent reading of online social photography within social markers, such as hashtags, arises due to two characteristics of social photography: (1) internal incongruence resulting from user-driven construction and (2) variability of content in terms of visual attributes, such as color combinations, brightness, and details in backgrounds. We suggest visual affluence—plenitude of visual stimuli, such as objects and surfaces containing a variety of color regions, present in visual imagery—as a basis for classifying visual content and image segmentation as a technique to measure affluence. We demonstrate that images containing objects with complex texture and background patterns are more affluent, while images that include blurry backgrounds are less affluent than others. Moreover, images that contain letters and dark, single-color backgrounds are less affluent than images that include subtle shades. Mann–Whitney U test results for ten pairs of hashtags showed that eight pairs had significant differences in visual affluence. The proposed measure can be used to encourage a “visually oriented” turn in online social photography research that can benefit from hybrid methods that are able to extrapolate micro-level findings to macro-level effects.