Reviewerly/exHarmony huggingface dataset:
Third-Person Effect and Hate Speech Censorship on Facebook

By recruiting 368 US university students, this study adopted an online posttest-only between-subjects experiment to analyze the impact of several types of hate speech on their attitudes toward hate speech censorship. Results showed that students tended to think the influence of hate speech on others was greater than on themselves. Their perception of such messages’ effect on themselves was a significant indicator of supportive attitudes toward hate speech censorship and of their willingness to flag hateful messages.
Deep learning feature selection to unhide demographic recommender systems factors

Extracting demographic features from hidden factors is an innovative concept that provides multiple and relevant applications. The matrix factorization model generates factors which do not incorporate semantic knowledge. This paper provides a deep learning-based method: DeepUnHide, able to extract demographic information from the users and items factors in collaborative filtering recommender systems. The core of the proposed method is the gradient-based localization used in the image processing literature to highlight the representative areas of each classification class. Validation experiments make use of two public datasets and current baselines. Results show the superiority of DeepUnHide to make feature selection and demographic classification, compared to the state of art of feature selection methods. Relevant and direct applications include recommendations explanation, fairness in collaborative filtering and recommendation to groups of users.
Unextendible product bases from tile structures and their local entanglement-assisted distinguishability

We completely characterize the condition when a tile structure provides an unextendible product basis (UPB), and construct UPBs of different large sizes in $\mathbb{C}^m\otimes\mathbb{C}^n$ for any $n\geq m\geq 3$. This solves an open problem in [S. Halder et al., Phys. Rev. A 99, 062329 (2019)]. As an application, we show that our UPBs of size $(mn-4\lfloor\frac{m-1}{2}\rfloor)$ in $\mathbb{C}^m\otimes\mathbb{C}^n$ can be perfectly distinguished by local operations and classical communications assisted with a $\lceil\frac{m}{2}\rceil\otimes\lceil\frac{m}{2}\rceil$ maximally entangled state.
Bridging the Gap Between AI and Healthcare Sides: Towards Developing Clinically Relevant AI-Powered Diagnosis Systems

Despite the success of Convolutional Neural Network-based Computer-Aided Diagnosis research, its clinical applications remain challenging. Accordingly, developing medical Artificial Intelligence (AI) fitting into a clinical environment requires identifying/bridging the gap between AI and Healthcare sides. Since the biggest problem in Medical Imaging lies in data paucity, confirming the clinical relevance for diagnosis of research-proven image augmentation techniques is essential. Therefore, we hold a clinically valuable AI-envisioning workshop among Japanese Medical Imaging experts, physicians, and generalists in Healthcare/Informatics. Then, a questionnaire survey for physicians evaluates our pathology-aware Generative Adversarial Network (GAN)-based image augmentation projects in terms of Data Augmentation and physician training. The workshop reveals the intrinsic gap between AI/Healthcare sides and solutions on Why (i.e., clinical significance/interpretation) and How (i.e., data acquisition, commercial deployment, and safety/feeling safe). This analysis confirms our pathology-aware GANs’ clinical relevance as a clinical decision support system and non-expert physician training tool. Our findings would play a key role in connecting inter-disciplinary research and clinical applications, not limited to the Japanese medical context and pathology-aware GANs.
Security-Driven hybrid collaborative recommendation method for cloud-based iot services

The rapid development of IoT (Internet of Things) systems and cloud techniques has paved the way for recommender systems to facilitate the daily life of users. However, the accompanying cybersecurity risks, such as environmental attacks and software attacks, must not be ignored. Thus, the security problem in recommender systems becomes a serious challenge for cloud-based IoT services. Moreover, most of existing collaborative recommendation algorithms mainly focus on user-item interaction relationships but seldom consider user-user or item-item co-occurrence relationships, which may affect prediction accuracy. To overcome the above shortcomings, this paper proposes a security-driven hybrid collaborative recommendation method to deal with the large-scale IoT services accessible by clouds in a more scalable and secure manner. Our proposal integrates the factorization-based latent factor model with the neighbor-based collaborative model to mine not only user-service interaction relationships but also user-user and service-service co-occurrence relationships. Moreover, the local sensitive hash (LSH) technique is adopted to speed up the neighbor searching and preserve users’ sensitive information for security concerns based on hash mapping. Finally, experiment results demonstrate that the proposed method can improve prediction accuracy while guaranteeing information security.
Predicting the Difficulty and Response Time of Multiple Choice Questions Using Transfer Learning

This paper reports on whether transfer learning can improve the prediction of the difficulty and response time parameters for ≈ 18,000 multiple-choice questions from a high-stakes medical exam.The type of the signal that best predicts difficulty and response time is also explored, both in terms of representation abstraction and item component used as input (e.g., whole item, answer options only, etc.).The results indicate that, for our sample, transfer learning can improve the prediction of item difficulty when response time is used as an auxiliary task but not the other way around.In addition, difficulty was best predicted using signal from the item stem (the description of the clinical case), while all parts of the item were important for predicting the response time.
Integrated photonic FFT for photonic tensor operations towards efficient and high-speed neural networks

Abstract The technologically-relevant task of feature extraction from data performed in deep-learning systems is routinely accomplished as repeated fast Fourier transforms (FFT) electronically in prevalent domain-specific architectures such as in graphics processing units (GPU). However, electronics systems are limited with respect to power dissipation and delay, due to wire-charging challenges related to interconnect capacitance. Here we present a silicon photonics-based architecture for convolutional neural networks that harnesses the phase property of light to perform FFTs efficiently by executing the convolution as a multiplication in the Fourier-domain. The algorithmic executing time is determined by the time-of-flight of the signal through this photonic reconfigurable passive FFT ‘filter’ circuit and is on the order of 10’s of picosecond short. A sensitivity analysis shows that this optical processor must be thermally phase stabilized corresponding to a few degrees. Furthermore, we find that for a small sample number, the obtainable number of convolutions per {time, power, and chip area) outperforms GPUs by about two orders of magnitude. Lastly, we show that, conceptually, the optical FFT and convolution-processing performance is indeed directly linked to optoelectronic device-level, and improvements in plasmonics, metamaterials or nanophotonics are fueling next generation densely interconnected intelligent photonic circuits with relevance for edge-computing 5G networks by processing tensor operations optically.
A novel method based on symbolic regression for interpretable semantic similarity measurement

The problem of automatically measuring the degree of semantic similarity between textual expressions is a challenge that consists of calculating the degree of likeness between two text fragments that have none or few features in common according to human judgment. In recent times, several machine learning methods have been able to establish a new state-of-the-art regarding the accuracy, but none or little attention has been paid to their interpretability, i.e. the extent to which an end-user could be able to understand the cause of the output from these approaches. Although such solutions based on symbolic regression already exist in the field of clustering, we propose here a new approach which is being able to reach high levels of interpretability without sacrificing accuracy in the context of semantic textual similarity. After a complete empirical evaluation using several benchmark datasets, it is shown that our approach yields promising results in a wide range of scenarios.
Analisis Keefektifan Pembelajaran Online di Masa Pandemi Covid-19

The purpose of this study was to obtain information about the effectiveness of the online learning process in the Covid-19 pandemic. The study uses explorative qualitative methods with an inductive approach. In this study, the respondents involved were 5 people from SD Banyuajuh 6 Kamal, consisting of 2 teachers, 2 student guardians, and 1 student. To maintain the confidentiality of respondents' identities, researchers gave the names of respondents P1, P2, P3, P4, and P5. The interviews were conducted in a structured manner with questions compiled and linked and developed with related literature. The results of this study are less effective online learning because of the lack of facilities and infrastructure as well as the unpreparedness of technological education.
Information Aware max-norm Dirichlet networks for predictive uncertainty estimation

Precise estimation of uncertainty in predictions for AI systems is a critical factor in ensuring trust and safety. Deep neural networks trained with a conventional method are prone to over-confident predictions. In contrast to Bayesian neural networks that learn approximate distributions on weights to infer prediction confidence, we propose a novel method, Information Aware Dirichlet networks, that learn an explicit Dirichlet prior distribution on predictive distributions by minimizing a bound on the expected max norm of the prediction error and penalizing information associated with incorrect outcomes. Properties of the new cost function are derived to indicate how improved uncertainty estimation is achieved. Experiments using real datasets show that our technique outperforms, by a large margin, state-of-the-art neural networks for estimating within-distribution and out-of-distribution uncertainty, and detecting adversarial examples.
A framework for pre-processing of social media feeds based on integrated local knowledge base

Most of the previous studies on the semantic analysis of social media feeds have not considered the issue of ambiguity that is associated with slangs, abbreviations, and acronyms that are embedded in social media posts. These noisy terms have implicit meanings and form part of the rich semantic context that must be analysed to gain complete insights from social media feeds. This paper proposes an improved framework for pre-processing of social media feeds for better performance. To do this, the use of an integrated knowledge base (ikb) which comprises a local knowledge source (Naijalingo), urban dictionary and internet slang was combined with the adapted Lesk algorithm to facilitate semantic analysis of social media feeds. Experimental results showed that the proposed approach performed better than existing methods when it was tested on three machine learning models, which are support vector machines, multilayer perceptron, and convolutional neural networks. The framework had an accuracy of 94.07% on a standardized dataset, and 99.78% on localised dataset when used to extract sentiments from tweets. The improved performance on the localised dataset reveals the advantage of integrating the use of local knowledge sources into the process of analysing social media feeds particularly in interpreting slangs/acronyms/abbreviations that have contextually rooted meanings.
Spatial distribution characteristics of soil and vegetation in a reclaimed area in an opencast coalmine

Opencast coal mining activities result in severe damage to soil and vegetation, and the reconstruction of soil and vegetation is critical in opencast coalmine areas. To better understand the spatial distribution characteristics of soil and vegetation in opencast coalmines, a spatial correlation analysis was conducted on the soil and vegetation properties of the Anjialing and Antaibao opencast coalmines in the Pingshuo coal mining area in China. A total of 70 sample points were selected using grid and transect methods, from which the soil and vegetation properties were determined. The spatial autocorrelation, spatial clustering, and spatial variability of the soil and vegetation indicators were analyzed using global Moran’s index, local Moran’s index, and the geostatistics method based on ArcGIS 10.2 or Geoda 7.0 analysis software. The soil and vegetation indicators were divided into three categories, including vegetation indicators (arbor quantity, AQ; average diameter at breast height, ADBH; herb coverage, HC; average tree height, ATH; canopy density, CD), soil physical properties (soil bulk density, BD; clay content; silt content; sand content; gravel content, GC), and soil chemical properties (soil organic matter, SOM; total nitrogen, TN; available phosphorus, AP; available potassium, AK; pH). There was a strong global autocorrelation among ADBH, clay content, AQ, HC, and SOM. Dumping and reclamation activities affected the spatial distribution characteristics of soil and vegetation. The “high-high” and “low-low” types in all the indicators possessed a large spatial distribution, and the soil and vegetation properties had relatively high spatial clustering. This study can provide a reference for the reclamation practice in opencast coalmine areas.
Cross-Site Scripting Guardian: A Static XSS Detector Based on Data Stream Input-Output Association Mining

The largest number of cybersecurity attacks is on web applications, in which Cross-Site Scripting (XSS) is the most popular way. The code audit is the main method to avoid the damage of XSS at the source code level. However, there are numerous limits implementing manual audits and rule-based audit tools. In the age of big data, it is a new research field to assist the manual auditing through machine learning. In this paper, we propose a new way to audit the XSS vulnerability in PHP source code snippets based on a PHP code parsing tool and the machine learning algorithm. We analyzed the operation sequence of source code and built a model to acquire the information that is most closely related to the XSS attack in the data stream. The method proposed can significantly improve the recall rate of vulnerability samples. Compared with related audit methods, our method has high reusability and excellent performance. Our classification model achieved an F1 score of 0.92, a recall rate of 0.98 (vulnerable sample), and an area under curve (AUC) of 0.97 on the test dataset.
An uncertainty view on complementarity and a complementarity view on uncertainty

Since the uncertainty about an observable of a system prepared in a quantum state is usually described by its variance, when the state is mixed, the variance is a hybrid of quantum and classical uncertainties. Besides that complementarity relations are saturated only for pure, single-quanton, quantum states. For mixed states, the wave–particle quantifiers never saturate the complementarity relation and can even reach zero for a maximally mixed state. So, to fully characterize a quanton it is not sufficient to consider its wave–particle aspect; one has also to regard its correlations with other systems. In this paper, we discuss the relation between quantum correlations and local classical uncertainty measures, as well as the relation between quantum coherence and quantum uncertainty quantifiers. We obtain a complete complementarity relation for quantum uncertainty, classical uncertainty, and predictability. The total quantum uncertainty of a d-paths interferometer is shown to be equivalent to the Wigner–Yanase coherence and the corresponding classical uncertainty is shown to be an entanglement monotone. The duality between complementarity and uncertainty is used to derive quantum correlations measures that complete the complementarity relations for $$l_{1}$$ -norm and $$l_{2}$$ -norm coherences. Besides, we show that Brukner–Zeilinger’s invariant information quantifies both the wave and particle characters of a quanton and we obtain a sum uncertainty relation for the generalized Gell-Mann’s matrices.
Philippine Twitter Sentiments during Covid-19 Pandemic using Multinomial Naïve-Bayes

News of the novel coronavirus (COVID-19) started circulating in the Philippines by the end of January 2020.Like any calamity or relevant news, people used social media platforms such as Twitter to voice their options.This paper examines the polarity of COVID -19 related opinions on Twitter from January to March 2020 by applying natural language processing.A total of 29,514 tweets were collected throughout the said dates, where 10% of which was manually labeled to train a Multinomial Naive Bayes classification model that achieved 72% accuracy.Results showed that 52% of the remaining tweets are positive, and 48% have negative sentiments.
Max-independent set and the quantum alternating operator ansatz

The maximum-independent set (MIS) problem of graph theory using the quantum alternating operator ansatz is studied. We perform simulations on the Rigetti Forest simulator for the square ring, [Formula: see text], and [Formula: see text] graphs and analyze the dependence of the algorithm on the depth of the circuit and initial states. The probability distribution of observation of the feasible states representing maximum-independent sets is observed to be asymmetric for the MIS problem, which is unlike the Max-Cut problem where the probability distribution of feasible states is symmetric. For asymmetric graphs, it is shown that the algorithm clearly favors the independent set with the larger number of elements even for finite circuit depth. We also compare the approximation ratios for the algorithm when we choose different initial states for the square ring graph and show that it is dependent on the choice of the initial state.
Pipelined Dynamic Scheduling of Big Data Streams

We are currently living in the big data era, in which it has become more necessary than ever to develop “smart” schedulers. It is common knowledge that the default Storm scheduler, as well as a large number of static schemes, has presented certain deficiencies. One of the most important of these deficiencies is the weakness in handling cases in which system changes occur. In such a scenario, some type of re-scheduling is necessary to keep the system working in the most efficient way. In this paper, we present a pipeline-based dynamic modular arithmetic-based scheduler (PMOD scheduler), which can be used to re-schedule the streams distributed among a set of nodes and their tasks, when the system parameters (number of tasks, executors or nodes) change. The PMOD scheduler organizes all the required operations in a pipeline scheme, thus reducing the overall processing time.
Face Detection and Recognition System for Enhancing Security Measures Using Artificial Intelligence System

Background/objectives: The major changes which come across face recognition are to find the age and gender of the person.This study is centered on face detection with voice and biometric technology.Methodology: During this study, it has been worked on the input camera which takes multiple shots of person.After that, the Cascade Classification algorithm has been used inside the application which creates the multiple human templates.So the facial features have been detected.After that, it saved in particular database with their unique ID.Furthermore, the verification process has been started by matching the templates inside the database.Through this process, the student attendance has been marked automatically.Findings: It has been presented that face detection with voice and biometric technology can enhance the security measures.Employee's attendance can be marked by simply detecting face that can increase punctuality.Students can be checked and marked by face detection process.As well, it can also be setup in banks to enhance the security by allowing authorized people only or who have accounts in that bank.Novelty/improvements: For finding the age and gender from a particular image, relevant techniques are discussed, with some new approaches for maintaining security.We discussed complete models with security measures in this research.
Compositional knockoff filter for high‐dimensional regression analysis of microbiome data

Abstract A critical task in microbiome data analysis is to explore the association between a scalar response of interest and a large number of microbial taxa that are summarized as compositional data at different taxonomic levels. Motivated by fine‐mapping of the microbiome, we propose a two‐step compositional knockoff filter to provide the effective finite‐sample false discovery rate (FDR) control in high‐dimensional linear log‐contrast regression analysis of microbiome compositional data. In the first step, we propose a new compositional screening procedure to remove insignificant microbial taxa while retaining the essential sum‐to‐zero constraint. In the second step, we extend the knockoff filter to identify the significant microbial taxa in the sparse regression model for compositional data. Thereby, a subset of the microbes is selected from the high‐dimensional microbial taxa as related to the response under a prespecified FDR threshold. We study the theoretical properties of the proposed two‐step procedure, including both sure screening and effective false discovery control. We demonstrate these properties in numerical simulation studies to compare our methods to some existing ones and show power gain of the new method while controlling the nominal FDR. The potential usefulness of the proposed method is also illustrated with application to an inflammatory bowel disease data set to identify microbial taxa that influence host gene expressions.
Development of a new scleral contact lens with encapsulated photodetectors for eye tracking

Most eye trackers nowadays are video-based, which allows for a relatively simple and non-invasive approach but also imposes several constraints in terms of necessary computing power and conditions of use (e.g., lighting, spectacles, etc.). We introduce a new eye tracker using a scleral lens equipped with photodiodes and an eyewear with active illumination. The direction of gaze is obtained from the weighted average of photocurrents (centroid) and communicated through an optical link. After discussing the optimum photodiodes configuration (number, layout) and associated lighting (collimated, Lambertian), we present prototypes demonstrating the high performances possibilities (0.11° accuracy when placed on an artificial eye) and wireless optical communication.
SAM

Network traffic classification categorizes traffic classes based on protocols (e.g., HTTP or DNS) or applications (e.g., Facebook or Gmail). Its accuracy is a key foundation of some network management tasks like Quality-of-Service (QoS) control, anomaly detection, etc. To further improve the accuracy of traffic classification, recent researches have introduced deep learning based methods. However, most of them utilize the privacy-concerned payload (user data). Besides, they generally do not consider the dependency of bytes in a packet, which we believe can be exploited for the more accurate classification. In this work, we treat the initial bytes of a network packet as a language and propose a novel Self-Attention based Method (SAM) for traffic classification. The average F1-scores of SAM on protocol and application classification are 98.62% and 98.93%. With the higher accuracy of SAM, better QoS and anomaly detection can be met.