Reviewerly/exHarmony huggingface dataset:
PrivAttNet: Predicting Privacy Risks in Images Using Visual Attention

Visual privacy concerns associated with image sharing is a critical issue that need to be addressed to enable safe and lawful use of online social platforms. Users of social media platforms often suffer from no guidance in sharing sensitive images in public, and often face with social and legal consequences. Given the recent success of visual attention based deep learning methods in measuring abstract phenomena like image memorability, we are motivated to investigate whether visual attention based methods could be useful in measuring psychophysical phenomena like “privacy sensitivity”. In this paper we propose PrivAttNet - a visual attention based approach, that can be trained end-to-end to estimate the privacy sensitivity of images without explicitly detecting sensitive objects and attributes present in the image. We show that our PrivAttNet model outperforms various SOTA and baseline strategies - a 1.6 fold reduction in L1 - error over SOTA and 7%-10% improvement in Spearman-rank correlation between the predicted and ground truth sensitivity scores. Additionally, the attention maps from PrivAttNet are found to be useful in directing the users to the regions that are responsible for generating the privacy risk score.
Two-Qubit Local Fisher Information Correlation beyond Entanglement in a Nonlinear Generalized Cavity with an Intrinsic Decoherence

In this paper, we study a Hamiltonian system constituted by two coupled two-level atoms (qubits) interacting with a nonlinear generalized cavity field. The nonclassical two-qubit correlation dynamics are investigated using Bures distance entanglement and local quantum Fisher information under the influences of intrinsic decoherence and qubit–qubit interaction. The effects of the superposition of two identical generalized coherent states and the initial coherent field intensity on the generated two-qubit correlations are investigated. Entanglement of sudden death and sudden birth of the Bures distance entanglement as well as the sudden changes in local Fisher information are observed. We show that the robustness, against decoherence, of the generated two-qubit correlations can be controlled by qubit–qubit coupling and the initial coherent cavity states.
OSC: An Online Self-Configuring Big Data Framework for Optimization of QoS

Big-data frameworks such as MapReduce/Hadoop or Spark have many performance-critical configuration parameters which may interact with each other in a complex way. Their optimal values for an application on a given cluster are affected by not only the application itself but also its input data. This makes offline auto-configuration approaches hard to be used in practice because the input data of an application may change at each run. To address this issue, we propose an Online Self-Configuring (OSC) approach that automatically determines the optimal parameter values for a given application. OSC synergistically integrates three key techniques. First, OSC leverages <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ensemble learning</i> to build a precise performance model for a given application. Second, it quantifies the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">importance</i> of the parameters and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">interaction intensity</i> between them to accelerate the genetic algorithm for searching optimal configuration parameters. Third, OSC supports an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">incremental modeling</i> approach to achieve low overhead of the models for online needs. These techniques allow OSC to effectively learn the characteristics of an application and optimize its performance by automatically adjusting the configurations at runtime. Our implementation of OSC atop MapReduce/Hadoop 2.6 improves performance by 60 percent on average and up to 120 percent compared with the state-of-the-art approach. Lastly, the performance benefit of an application running on OSC generally increases along with its input data size.
A Proposed Intelligent Features Selection Method Using Meerkat Clan Algorithm

Abstract The feature selection process stand to select the subset of feature from the set of datasets to get the relevant an important features and remove irrelevant features. The feature selection types are supervised, unsupervised or semi-supervised. The feature selection methods are divided into: traditional methods such as Correlation and Information Gain or others and intelligent methods such as fuzzy logic, or use the swarm intelligence methods in feature selection such as Ant colony and Bees colony. This paper proposes an Intelligent Features Selection method based on Meerkat Clan Algorithm (IFS\MCA) system, when it used the modern MCA as first time to use the algorithm in feature selection to select the best subset of feature. This proposal used four set of dataset which as (Fri_c4, Sonar, Scene, Satellite), the efficiency of the proposal in feature selection is powerful and high accuracy when comparing with other standard methods in feature selection which as Correlation and Information gain when use the same set of dataset. The proposed IFS\MCA system uses the Mean Absolute Deviation (MAD) as a proposed choice as a fitness function (which is used as a first time with feature selection process).
A framework for association rule learning with social media networks

Abstract We present an application of association rule learning to analyze Twitter account follow patterns. In doing so, we develop a basic framework and tutorial for future researchers to build on, which takes advantage of the Twitter API. To demonstrate the method, we take samples of Twitter accounts following Joe Biden and Donald Trump. For each account in our sample population, we pull the account’s 100 most recently followed accounts. This data is cleaned and formatted for use with Python’s apyori package, which uses the well-known apriori algorithm to learn association rules for a given dataset. This work has two objectives: (1) demonstrate the application association rule learning to social media networks and (2) perform exploratory analysis on the resulting association rules. We successfully demonstrate association rule learning in a Jupyter-notebook environment with Python. The resulting association rules indicate some interesting similarities and differences in the networks of Biden’s and Trump’s Twitter followers. The demonstrated method can be generalized to any non-private Twitter account(s). Extensions of our work can apply the method to larger datasets, with a focus on analyzing the learned association rules. Our study demonstrates an innovative application of association rule learning outside of the traditional use cases, which suggests similar opportunities in fields such as politics, education, public health, and more.
Output statistics of quantum annealers with disorder

We demonstrate that the output statistics of a standard quantum annealing protocol run on D-Wave 2000Q can be mimicked by static disorder garnishing an otherwise ideal device hardware -- for a 10-qubit toy Hamiltonian as well as for problem instances with thousands of qubits. A Boltzmann-like distribution over distinct output states is shown to emerge with increasing disorder strength.
MixCaps: Capsules With Iteration Free Routing

In this paper, we propose a new variant of Capsule Networks called MixCaps. It is a new architecture that significantly decreases the compute capability required to run capsule networks. Due to the nature of our modules, we propose a new routing algorithm that does not require multiple iterations. All routing models prior to this architecture uses multiple iterations. This decreases our model's memory requirements by a significant margin unlike previous methods. This also provides us with the advantage to use both Matrix and Vector Poses. The model learns better complex representations as an aftereffect. Despite all this, we also show that our model performs on par with all prior capsule architectures on complex datasets such as Cifar-10 and Cifar-100.
Design and development of a web App for ICT and intercultural competences professor self-training

This article describes the design and development of a web app to improve the intercultural and ICT competences of university professors, allowing them to create virtual intercultural learning environments. It proposes approaches based on principles of self-training, virtual education, interculturality, and universal learning design. Its development is adopted from the ADDIE instructional design model. The App considers the phenomenon of the use of technology as a means for self-training and appropriation of competences in virtual intercultural learning environments.
CHNE: Context-aware Heterogeneous Network Embedding

A heterogeneous information network is a network with different kinds of nodes and edges, which can express different interrelationships between objects in a single graph. If we use heterogeneous information networks for analysis, we can take advantage of more information for analysis than homogeneous information networks, thus improve the quality of analysis results. Recently, many embedding processes are used to vectorize network data so that they can be used for analysis. Network embedding is a very important process in the process of network analysis, with the aim of producing vectors that express network information well, such as network structure and node features. There have been several recent network embedding studies which enhance the embedding performance by considering both network structure and text features of nodes in a network. But they are difficult to apply to heterogeneous information networks, because their network structures are different from each other. In this paper, therefore, we propose CHNE, a new heterogeneous information network embedding model which takes advantage of all interrelationships, text information and node types of heterogeneous information networks.
A Modified Approach for the Segmentation of Unconstrained Cursive Modi Touching Characters Cluster

In this paper, a robust character segmentation approach for cursive handwritten Modi script touching character cluster is presented. Prior to segmentation, the middle text region of the touching character cluster is separated by examining the location of Shirorekha and baseline. The middle text region is scrutinized for the estimation of ligature between two characters. Two different strategies are employed to find the location of the ligature. The selection of the strategy is based on the degree of connected component overlapratio. The foreground pixel intensity and vertical projection profile is scrutinized to segment the touching characters. The performance of the system is tested using the touching character clusters of the original archaic handwritten Modi documents. The proposed approach yields efficient touching characters cluster segmentation output and it is feasible to tackle most of the challenges in touching character cluster segmentation.
A fine-grained classification and security analysis of web-based virtual machine vulnerabilities

Web-based virtual machines are one of the primary targets of attackers due to number of design flaws they contain and the connectivity provided by the Web. The design and implementation of Inscription, the first fully automated Adobe Flash binary code transformation system that can guard major Flash vulnerability categories without modifying vulnerable Flash VMs, is presented and evaluated. Inscription affords a means of mitigating the significant class of web attacks that target unpatched, legacy Flash VMs and their apps. This new enforcement capability is most effective when supplied with security policies that accurately characterize VM security vulnerabilities and their mitigations. Researchers and security engineers commonly depend on well-known, public vulnerability databases that document such vulnerabilities and provide details about each; but vulnerability information that is inconsistent, inaccurate, or vague hinders diagnosis of vulnerabilities residing in the implementations of web-based VMs, which is one of the crucial prerequisites of building generic, comprehensive security solutions for mitigating them. For example, a large percentage of disclosed vulnerabilities of the ActionScript VM (AVM), which executes Flash binaries, are vaguely classified as “Memory Corruption” or “Unspecified”. Deeper analysis of these vulnerabilities reveals that most can be more precisely classified as (1) use-after-free, (2) double-free, (3) integer overflow, (4) buffer overflow, or (5) heap overflow vulnerability sub-classes. To improve web vulnerability analysis and mitigation, a more thorough, comprehensive and accurate reclassification of web-based vulnerabilities is presented, in which “Memory Corruption” and “Unspecified” vulnerabilities are reclassified into one of these fine-grained vulnerability sub-classes.
Optimal Anticodes, Diameter Perfect Codes, Chains and Weights

Let P be a partial order on [n] = {1,2,...,n}, F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</sub> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sup> be the linear space of n-tuples over a finite field F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</sub> and w be a weight on F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</sub> . In this paper, we consider metrics on \mathbb F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</sub> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sup> induced by chain orders P over [n] and weights w over \mathbb F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</sub> , and we determine the cardinality of all optimal anticodes and completely classify them. Moreover, we determine all diameter perfect codes for a set of relevant instances on the aforementioned metric spaces.
Research on STM32 Development Board Based on ARM Cortex-M3

With the improvement of science and technology, the application of ARM is more and more extensive, Cortex-M3 is a new product launched by ARM. Cortex-M3 is a 32-bit processor core, using ARMv7-M architecture, which has high advantages in low cost and low power consumption. In this paper, the ARM Cortex-M3 and STM32 microcontrollers are studied. By selecting a semiconductor STM32 F103ZET6 development board, the basic module GPIO of STM32 is studied, and then the timer is used to generate PWM (pulse width modulation) output. PWM is an effective technique that uses the digital output of a microprocessor to get command of analog circuits, and is widely applied in many fields from measurement and communication to rate of work command and conversion. Therefore, the study of PWM technology has very important practical significance [1] [2]. The time of the timer is the period of the PWM. By calculating the square wave frequency and duty cycle, the timer and IO port are configured, and finally the PWM output is completed.
Underwater target tracking method based on convolutional neural network

In order to solve the problems of low accuracy of underwater target tracking, poor real-time performance and large amount of calculation required, an underwater target tracking method based on the improved SiamRPN++ algorithm is adopted. By selecting the inverted residual bottleneck block to construct a new backbone network SmallMobileNet, instead of the backbone network ResNet-50 of the SiamRPN++algorithm, the use of deep separable convolution to reduce the amount of calculation, while ensuring accuracy and real-time performance, adjust The number of channels, layers, parameters of the network and the complexity of each segment of the network are used to reduce the computational cost and hardware requirements, so that the algorithm can be transplanted to the underwater tracking platform. Through experiments, compared with the original algorithm, the accuracy of the SiamRPN++ algorithm with Small-MobileNet as the backbone network is improved, the amount of network parameters and calculations are reduced, and the tracking speed is improved, which verifies the effectiveness of the method.
Unsupervised Word Segmentation with Bi-directional Neural Language Model

We propose an unsupervised word segmentation model, in which for each unlabelled sentence sample, the learning objective is to maximize the generation probability of the sentence given its all possible segmentations. Such a generation probability can be factorized into the likelihood of each possible segment given the context in a recursive way. To capture both the long- and short-term dependencies, we propose to use a bi-directional neural language model to better extract the features of the segment’s context. Two decoding algorithms were also developed to combine the context features from both directions to generate the final segmentation at the inference time, which helps to reconcile word-boundary ambiguities. Experimental results show that our context-sensitive unsupervised segmentation model achieved state-of-the-art at different evaluation settings on various datasets for Chinese, and the comparable result for Thai.
Artificial Intelligence in Social Media

Artificial intelligence (AI) is a branch of computer science that deals with the capability of a machine to imitate human intelligent behavior. It has the potential to help tackle some of the world’s most challenging social problems. Modern social media, also known as social networking, include Facebook, Twitter, Instagram, Pinterest, and YouTube. AI is a fundamental component of how today’s social networks function. The use of AI in social media is growing in an unprecedented way and is constantly transforming social media. This paper explores various artificial intelligence tools and their impact on social media companies.
Estimation of the Number of Clusters based on Simplical Depth

The estimation of the cluster numbers is one of the most significant open research problem in clustering domain. The clustering accuracy highly depends on the accurate number of clusters. We propose a method based on simplical depth, namely SDM, in this paper to estimate the number of clusters. We use the recursive feature elimination process to select two most significant features from a given dataset. We perform an initial partition on the selected features for exploring the compactness of clusters. We use simplicial depth method to estimate depth values of the data points in each cluster. From the initial partition, the number of clusters with the highest depth value is considered as the estimated number of clusters. To evaluate the performance of proposed method, we use five state-of-the-art methods on both synthetic and real datasets. We observe that the accuracy of cluster number estimation with simplical depth based method is better than others in most of the cases, which is the key element for the robust and compact clustering solution.
The Impact of Integrating ICT in Malay Foreign Language Teaching and Learning

Abstract COVID-19 affects 213 countries and territories around the world. This pandemic prompts substantial negative repercussions on social, health, economic, education, and tourism domains. New norms of life activities are designed as a solution so that life can be continued as before. This situation includes the education field, whereby various methods are created so that the progression of learning and teaching can be carried out even in limited circumstances. An imperative solution was chosen to integrate ICT into this field. Thus, the ICT applications in teaching and learning were the researchers’ focus of attention. This research centred predominantly on Malay foreign language teaching and learning that must adapt and renew itself to be compatible with the ICT. The goal of this study was to examine the impact of ICT and highlight the positive effects of it in order to keep up with the prevailing situation of the coronavirus COVID-19 pandemic. The writing of this paper was based on research focusing on Malay foreign language teaching and learning using ICT. Studies revealed that the application of ICT in language learning begins to get recognition from educators. Although there are some strengths in integrating the ICT on the other, it still holds considerable vulnerability which requires improvements to be made in order to meet teaching and learning requirements. Therefore, this study further illustrated the level of impact of integrating ICT in the education as a source of reference to the educator and education apps developer who created more sophisticate apps and fulfilled the demand of education. This analysis too would become a reference to literature review in extemporising the quality of teaching and learning in Malay foreign language.
Evaluating the impact of covariate lookback times on performance of patient-level prediction models

Abstract Background The goal of our study is to examine the impact of the lookback length when engineering features to use in developing predictive models using observational healthcare data. Using a longer lookback for feature engineering gives more insight about patients but increases the issue of left-censoring. Methods We used five US observational databases to develop patient-level prediction models. A target cohort of subjects with hypertensive drug exposures and outcome cohorts of subjects with acute (stroke and gastrointestinal bleeding) and chronic outcomes (diabetes and chronic kidney disease) were developed. Candidate predictors that exist on or prior to the target index date were derived within the following lookback periods: 14, 30, 90, 180, 365, 730, and all days prior to index were evaluated. We predicted the risk of outcomes occurring 1 day until 365 days after index. Ten lasso logistic models for each lookback period were generated to create a distribution of area under the curve (AUC) metrics to evaluate the discriminative performance of the models. Calibration intercept and slope were also calculated. Impact on external validation performance was investigated across five databases. Results The maximum differences in AUCs for the models developed using different lookback periods within a database was &lt; 0.04 for diabetes (in MDCR AUC of 0.593 with 14-day lookback vs. AUC of 0.631 with all-time lookback) and 0.012 for renal impairment (in MDCR AUC of 0.675 with 30-day lookback vs. AUC of 0.687 with 365-day lookback ). For the acute outcomes, the max difference in AUC across lookbacks within a database was 0.015 (in MDCD AUC of 0.767 with 14-day lookback vs. AUC 0.782 with 365-day lookback) for stroke and &lt; 0.03 for gastrointestinal bleeding (in CCAE AUC of 0.631 with 14-day lookback vs. AUC of 0.660 with 730-day lookback). Conclusions In general the choice of covariate lookback had only a small impact on discrimination and calibration, with a short lookback (&lt; 180 days) occasionally decreasing discrimination. Based on the results, if training a logistic regression model for prediction then using covariates with a 365 day lookback appear to be a good tradeoff between performance and interpretation.