Reviewerly/exHarmony huggingface dataset:
Efficient secret sharing scheme with cheater identification based on QR code

Secret sharing is an effective way of protecting secret messages.However, the traditional secret sharing schemes are considered meaningless due to malicious people attention which might raise risks.To overcome the weakness, this paper presents an effective secret sharing scheme with the functionality of cheater identification, based on meaningful QR code.The secret message will be split and concealed in the padding region of cover QR codes with the assistance of Latin square and it can be completely restored when all the involved participants cooperate.The concealing strategy exploits the characteristic of Reed-Solomon (RS) code to ensure the strong robustness of generated QR code pseudo-shares.The meaningful QR code pseudo-shares help to reduce the curious of unrelated persons.Some experiments were done to evaluate the performance of proposed scheme.The results showed that the proposed scheme is feasible, efficient and secure compared to the other existing schemes.It also achieves a higher secret payload and maintains stronger robustness.
A COMPARISON OF ACCURACY BETWEEN TWO METHODS: NAЇVE BAYES ALGORITHM AND DECISION TREE-J48 TO PREDICT THE STOCK PRICE OF PT ASTRA INTERNATIONAL Tbk USING DATA FROM INDONESIA STOCK EXCHANGE

The ability to predict the stock prices is very important for market players, whether individual or organizational investors. The market players needs to know how to predict, that will help them in their decision making process, whether to buy or to sell its shares, so that it can maximize profits and reduce potential losses due to mistakes in decision making. In accordance to this, the authors conducted a study that aimed to analyze and to compare the accuracy of two (2) methods that is used to predict the stock prices, namely: the Naїve Bayes Method and the Decision Tree-J48 Method. The amount of data used in this study were 1,195 stock datas ofPT Astra International Tbk, issued by the IDX, by the period of January 1, 2013 to November 30, 2017.This study uses 7 attributes, namely: Previews, High, Low, Close, Volume, Value, and Frequency. By using the WEKA application the result shows that, the accuracy of the Naïve Bayes Method using 20% of testing data, is 92.0502%, the precision value is 0.920 and the value of recall is 0.961, while the accuracy of the Decision Tree J-48 method, using 20% of testing data, is 98.7448%, with precision value of 0.989 and the value of recall of0.997.Through this results, it can be concluded that the decision tree J-48 algorithm has a better accuracy results compared to the Naive Bayes algorithm in predicting the stock price of PT. Astra Internasional Tbk.
UAV Navigation System with Obstacle Detection using Deep Reinforcement Learning with Noise Injection

This paper is an application of deep reinforcement learning and noise injection to the navigation of the Unmanned Aerial Vehicle. Recent studies take advantage of the enhancement of the exploratory of its navigation to the environment like civilian missions such as disaster monitoring and, search and rescue. Some research studies include noise injection in the deep learning algorithm to improve its performance. Therefore, this study applied the ad compared two ways to inject Gaussian noise namely Gaussian Noise Layer and Noisy Network in Double Dueling Deep Q Network. The tests conducted with the Gaussian Noise Layer with a standard deviation of 1.0 gives stable exploration performance in terms of q learning, loss and flight navigation. The noticeable results give the significance to further research in Gaussian Noise injection in optimizing the deep reinforcement algorithm of unmanned aerial vehicles where can be further applied to future technology of navigation.
Artifacts Reduction for Compression Image with Pyramid Residual Convolutional Neural Network

A pyramid residual convolutional neural network (PRCNN) is proposed to restore image with compression artifacts in this paper, and the method is enable to process multiple resolutions by fixing patch size extracted from whole image. Convolutional neural network has reached great performance in image processing (e.g. denoise, deblur, super-resolution), however deeper network may cause vanishing or exploding gradient problems, and it is hard to apply in realistic scene for high complexity. Thus, the residual blocks (RB) are proposed to balance between performance and application, besides, this paper exploits pyramid convolutional neural network to learn coarse-fine feature. In order to handle various resolutions, the fixed patch based method is used to adapt realistic scene. The experiment shows that the proposed algorithm can reduce compression artifacts through objective and subjective assessment, and the training/testing data are collected with H.264 coding. The proposed method can improve PSNR and SSIM from 0.54dB to 1.41dB, 0.01 to 0.04 while compression artifacts are reduced in visual quality, respectively.
Approximate k values using Repulsive Force without Domain Knowledge in k-means

The k-means algorithm is widely used in academia and industry due to easy and simple implementation, enabling fast learning for complex datasets.However, k-means struggles to classify datasets without prior knowledge of specific domains.We proposed the repulsive k-means (RK-means) algorithm in a previous study to improve the k-means algorithm, using the repulsive force concept, which allows deleting unnecessary cluster centroids.Accordingly, the RK-means enables to classifying of a dataset without domain knowledge.However, three main problems remain.The RK-means algorithm includes a cluster repulsive force offset, for clusters confined in other clusters, which can cause cluster locking; we were unable to prove RK-means provided optimal convergence in the previous study; and RK-means shown better performance only normalize term and weight.Therefore, this paper proposes the advanced RK-means (ARK-means) algorithm to resolve the RK-means problems.We establish an initialization strategy for deploying cluster centroids and define a metric for the ARK-means algorithm.Finally, we redefine the mass and normalize terms to close to the general dataset.We show ARK-means feasibility experimentally using blob and iris datasets.Experiment results verify the proposed ARK-means algorithm provides better performance than k-means, k '-means, and RK-means.
Semi-Implicit Stochastic Recurrent Neural Networks

Stochastic recurrent neural networks with latent random variables of complex dependency structures have shown to be more successful in modeling sequential data than deterministic deep models. However, the majority of existing methods have limited expressive power due to the Gaussian assumption of latent variables. In this paper, we advocate learning implicit latent representations using semi-implicit variational inference to further increase model flexibility. Semi-implicit stochastic recurrent neural network (SIS-RNN) is developed to enrich inferred model posteriors that may have no analytic density functions, as long as independent random samples can be generated via reparameterization. Extensive experiments in different tasks on real-world datasets show that SIS-RNN outperforms the existing methods.
Hybrid Sequential Recommender via Time-aware Attentive Memory Network.

Recommendation systems aim to assist users to discover most preferred contents from an ever-growing corpus of items. Although recommenders have been greatly improved by deep learning, they still faces several challenges: (1) Behaviors are much more complex than words in sentences, so traditional attentive and recurrent models may fail in capturing the temporal dynamics of user preferences. (2) The preferences of users are multiple and evolving, so it is difficult to integrate long-term memory and short-term intent. In this paper, we propose a temporal gating methodology to improve attention mechanism and recurrent units, so that temporal information can be considered in both information filtering and state transition. Additionally, we propose a Multi-hop Time-aware Attentive Memory network (MTAM) to integrate long-term and short-term preferences. We use the proposed time-aware GRU network to learn the short-term intent and maintain prior records in user memory. We treat the short-term intent as a query and design a multi-hop memory reading operation via the proposed time-aware attention to generate user representation based on the current intent and long-term memory. Our approach is scalable for candidate retrieval tasks and can be viewed as a non-linear generalization of latent factorization for dot-product based Top-K recommendation. Finally, we conduct extensive experiments on six benchmark datasets and the experimental results demonstrate the effectiveness of our MTAM and temporal gating methodology.
An Efficient Shared-memory Parallel Sinkhorn-Knopp Algorithm to Compute the Word Mover's Distance.

The Word Mover's Distance (WMD) is a metric that measures the semantic dissimilarity between two text documents by computing the cost of moving all words of a source/query document to the most similar words of a target document optimally. Computing WMD between two documents is costly because it requires solving an optimization problem that costs \(O(V^3log(V))\) where \(V\) is the number of unique words in the document. Fortunately, the WMD can be framed as the Earth Mover's Distance (EMD) (also known as the Optimal Transportation Distance) for which it has been shown that the algorithmic complexity can be reduced to \(O(V^2)\) by adding an entropy penalty to the optimization problem and a similar idea can be adapted to compute WMD efficiently. Additionally, the computation can be made highly parallel by computing WMD of a single query document against multiple target documents at once (e.g., finding whether a given tweet is similar to any other tweets happened in a day). In this paper, we present a shared-memory parallel Sinkhorn-Knopp Algorithm to compute the WMD of one document against many other documents by adopting the \(O(V^2)\) EMD algorithm. We used algorithmic transformations to change the original dense compute-heavy kernel to a sparse compute kernel and obtained \(67\times\) speedup using \(96\) cores on the state-of-the-art of Intel\textregistered{} 4-sockets Cascade Lake machine w.r.t. its sequential run. Our parallel algorithm is over \(700\times\) faster than the naive parallel python code that internally uses optimized matrix library calls.
Adversarial Machine Learning in Recommender Systems: State of the art and Challenges.

Latent-factor models (LFM) based on collaborative filtering (CF), such as matrix factorization (MF) and deep CF methods, are widely used in modern recommender systems (RS) due to their excellent performance and recommendation accuracy. Notwithstanding their great success, in recent years, it has been shown that these methods are vulnerable to adversarial examples, i.e., subtle but non-random perturbations designed to force recommendation models to produce erroneous outputs. The main reason for this behavior is that user interaction data used for training of LFM can be contaminated by malicious activities or users' misoperation that can induce an unpredictable amount of natural noise and harm recommendation outcomes. On the other side, it has been shown that these systems, conceived originally to attack machine learning applications, can be successfully adopted to strengthen their robustness against attacks as well as to train more precise recommendation engines. In this respect, the goal of this survey is two-fold: (i) to present recent advances on AML-RS for the security of RS (i.e., attacking and defense recommendation models), (ii) to show another successful application of AML in generative adversarial networks (GANs), which use the core concept of learning in AML (i.e., the min-max game) for generative applications. In this survey, we provide an exhaustive literature review of 60 articles published in major RS and ML journals and conferences. This review serves as a reference for the RS community, working on the security of RS and recommendation models leveraging generative models to improve their quality.
Virtualidad y cultura digital en las experiencias escolares infantiles: una etnografía visual en contexto de pobreza

This paper presents partial results from a research centered on children&rsquo;s social inclusion/ exclusion processes in a marginalized school of Santiago de Chile. We realized an interpretative and visual school ethnography, focused on a 4th grade. The data produced are journal diaries (observations and ethnographic interviews); children&rsquo;s visual productions (photographs and drawings); and children&rsquo;s group interviews transcriptions. These data reveal the centrality of the virtual universe and the digital references in children&rsquo;s experiences and in their subjectivations. The digital culture would extend the field of real for children, allowing them to subjectivize and produce new knowledges. It appears as a medium to deploy the virtual experience, a &ldquo;transitional object&rdquo; that enables to connect the internal world with the external world. The virtual space seems fundamental for children&rsquo;s subjectivations and should not be considered as a negative or threatening aspect for school, rather as a potential space to bring the children to school knowledges. This depends on its exclusive mediation by digital technologies or its mediation by the other adult, through the inclusion of virtuality &mdash;digital as no digital&mdash; in the intermediate space of communication.
The Innovation: A Journal to See the Unseen and Change the Unchanged

Humanity is standing at the beginning of the Fourth Industrial Revolution and is ready to embrace the digital era. The diversification and specialization of natural science have contributed to re-shaping the society in which we live. Meanwhile, humanity is also facing emergent global challenges, including climate change, emerging infectious diseases, and socioeconomic inequality. The time is ripe to adopt innovative approaches to integrate various disciplines to tackle the issues of sustainable development worldwide. As humankind unites into a global village (Figure 1), we have the opportunity to encourage innovation that builds on integration of research across disciplines. To promote interdisciplinary collaborations and highlight combined productivity, we have launched a new journal, The Innovation, to display inspiring multidisciplinary discoveries for the science community, to encourage researchers to focus on the nature of science and the core of the free scientific exploration. The Innovation has been co-founded by over 100 scientists from all research disciplines. Most of them are members of the Youth Innovation Promotion Association, Chinese Academy of Sciences. They are engaged in cutting-edge research, pushing the envelope of possibilities, while keeping their fingers on the pulse of change. Bearing passion, ideas, and spirit, they aspire to construct an academic platform to facilitate innovation in the new times. The Innovation values the advancement of science and its applications. Thinking out of the box is both appreciated and encouraged. We aim to see the unseen, change the unchanged, and promote the transition from basic research to applied technology. The Innovation is an open access online journal to promptly publish peer-reviewed Articles, Reports, Reviews, Editorials, News & Buzz, and others in all fields on the basis of novelty, demonstrated reproducibility, broad interest, and significant impact. To better serve the community, the journal will include Public Summary, Graphic Abstract, and related Commentary & Views. A number of methods, including social media, short videos, and comments/editorials, will be used. The goal is to make quality research speedily and widely known, increasing the impact of scientific breakthrough. The Innovation will adhere to the values of integrity, professionalism, and diversity. As a leading scientific journal, it is about to break new ground and to lead the way into the future, flying the flag for innovation among interdisciplinary science in this flourishing age.
Combination of Rail Fence Cipher Algorithm and Least Significant Bit Technique to Secure the Image File

Abstract Image is a visual representation of an object The process of sending information, especially images, can be done anywhere and with any intermediary, but not all information is public. Sometimes someone needs privacy or security from data or information that will be sent or received. One way to secure image files is to use cryptography. Cryptography is the study of how to keep data or messages safe when sent, from the sender to the recipient, without experiencing interference from third parties. In this study cryptography used is Rail Fence Cipher that was combined with steganography, so that the secret message could not be seen in plain view. Steganography serves to hide secret messages in other messages, so the secret is a very hidden existence. There are several methods in Steganography, one of which is Least Significant Bit. The concept of Least Significant Bit works by replacing the last bit of RGB image, where the message is converted first into bits. Based on experiments conducted on the Rail Fence Cipher algorithm, the time of encryption and decryption needed when the resolution of the image gets larger is linearly proportional to the value. The image does not show significant changes before and after inserting a secret message.
DARK PATTERNS IN THE MEDIA: A SYSTEMATIC REVIEW

This paper addresses the topic of dark patterns used in the design of various web products or services. Dark patterns are deceptive elements that are intentionally crafted to make the users do actions that they wouldn’t do otherwise. Those techniques are used for the benefit of various stakeholders and are included in web products that are used world-wide, such as social media platforms, some popular apps or web services. The concept is well known among practitioners. However, a few studies on this topic have been done and in order to gain awareness about it some more academic research is needed. The main purpose of this study is o investigate if there is some consent about the topic of dark patterns among the digital media resources. This study is of descriptive nature. A systematic review approach is used, analyzing over 30 original digital media sources that addresses this topic. This paper is not about criticizing, it’s about continuing an ongoing discussion about the users’ rights and the values of web products creators with the purpose of minimizing malicious techniques towards users. This study contributes to the broadening of the research on dark patterns. It also contributes to the understanding of those techniques in an effort to minimize them. Results can be used for developing frameworks for future research.
Extracting Short Entity Descriptions for Open-World Extension to Knowledge Graph Completion Models

Great advances have been made in closed-world Knowledge Graph Completion (KGC). But it still remains a challenge for open-world KGC. A recently proposed open-world KGC model called OWE found a method to map the text space embedding obtained from the entity name and description to a pre-trained graph embedding space, by which OWE can extend the embedding-based KGC models to the open world. However, OWE uses average aggregation to obtain the text representation, no matter the entity description is long or short. It uses much unnecessary textual information and may become unstable. In this paper, we propose an extension to OWE, which is named OWE-MRC, to extract short expressions for entities from long descriptions by using a Machine Reading Comprehension (MRC) model. After obtaining short descriptions for entities, OWE-MRC uses the extension method of OWE to extend the embedding-based KGC models to the open world. We have applied OWE-MRC to extend common KGC models, such as ComplEx and Graph Neural Networks (GNNs) based models, to perform open-world link prediction. Our experiments on two datasets FB20k and DBPedia50k indicate that (1) the MRC model can effectively extract meaningful short descriptions; (2) our OWE-MRC uses much less textual information than OWE, but achieves competitive performance on open-world link prediction. In addition, we have used OWE to extend the GNN-based model to the open world. And our extended GNN model has achieved significant improvements on open-world link prediction comparing to the state-of-the-art open-world KGC models.
Testing dataset for head segmentation accuracy for the algorithms in the ‘BGSLibrary’ v3.0.0 developed by Andrews Sobral

This dataset consists of video files that were created to test the accuracy of background segmentation algorithms contained in the C++ wrapper ‘BGSLibrary’ v3.0.0 developed by Andrews Sobral. The comparison is based on segmentation accuracy of the algorithms on a series of indoor color-depth video clips of a single person's head and upper body, each highlighting a common factor that can influence the accuracy of foreground-background segmentation. The algorithms are run on the color image data, while the ‘ground truth’ is semi-automatically extracted from the depth data. The camera chosen for capturing the videos features paired color-depth image sensors, with the color sensor having specifications typical of mobile devices and webcams, which cover most of the use cases for these algorithms. The factors chosen for testing are derived from a literature review accompanying the dataset as being able to influence the efficacy of background segmentation. The assessment criteria for the results were set based on the requirements of common use cases such as gamecasting and mobile communications to allow the readers to make their own judgements on the merits of each algorithm for their own purposes.
Proposal of Adaptive Randomness in Differential Evolution

Differential evolution (DE) is a widely used optimization algorithm, which can achieve high accuracy with a simple mechanism, but sometimes have only limited performances due to its simplicity. In order to mitigate the inappropriate effect of poor initial search points, it is known that adding a random search to DE contributes to obtain better results than normal DE. However, it is inefficient to perform many random searches when the search process is almost converged. In this study, we propose a novel method of DE with Adaptive Randomness (DEAR), which is a hybrid of two promising algorithms of DIEtoDE and SaDE, and can adaptively change the frequency of random search maintaining efficiency. Numerical experiments demonstrated that the proposed method can identify better solutions than other comparative methods.
Performance Improvement of Kotlin Program in Consideration of JIT Compiler Optimization

Kotlin is a programming language that has high-level interoperability with Java. It was determined as the first-class language for Android application development in 2019 and is expected to increase its importance in client devices, such as smartphones and PCs, in the consumer electronics field. However, this is a newly emerging language and its performance has not been studied enough. In this paper, we focus on a simple program function, which is a for statement, and discuss its performance. First, we evaluate the performance of iteration with for statement written in Kotlin and Java and show that there is a significant performance difference. We then compare the Java virtual machine (JVM) bytecodes delivered from Kotlin and Java source codes and discuss the cause of this performance. Based on this comparison, we propose a method for improving the performance of an application written in Kotlin in Windows JVM with JIT (Just in time) compiler. We then show that our proposed method can improve the performance of processing a for statement of Kotlin.