Reviewerly/exHarmony huggingface dataset:
A General Cipher for Individual Data Anonymization

Over the years, the literature on individual data anonymization has burgeoned in many directions. While such diversity should be praised, it does not come without some difficulties. Currently, the task of selecting the optimal analytical environment is complicated by the multitude of available choices and the fact that the performance of any method is generally dependent of the data properties. In light of these issues, the contribution of this paper is twofold. First, based on recent insights from the literature and inspired by cryptography, it proposes a new anonymization method that shows that the task of anonymization can ultimately rely only on ranks permutations. As a result, the method offers a new way to practice data anonymization by performing it ex-ante and independently of the distributional features of the data instead of being engaged, as it is currently the case in the literature, in several ex-post evaluations and iterations to reach the protection and information properties sought after. Second, the method establishes a conceptual connection across the field, as it can mimic all the currently existing tools. To make the method operational, this paper proposes also the introduction of permutation menus in data anonymization, where recently developed universal measures of disclosure risk and information loss are used ex-ante for the calibration of permutation keys. To justify the relevance of their uses, a theoretical characterization of these measures is also proposed.
Subtitling for the deaf and hard-of-hearing in immersive environments : results from a focus group

Immersive media such as virtual reality or 360o content is increasingly present in our society. However, immersive content is not always accessible to all, and research is needed on how to cater for the needs of different kinds of users. This article will review the current situation of immersive technologies and their applications in media. Also, research studies carried out so far concerning subtitling and SDH in immersive media will be discussed, as well as current implementation of subtitles in immersive content, such as VR video games or 360o videos. Finally, the results from a focus group carried out in Spain with deaf and hard-of-hearing users, as well as professional subtitlers, on how to subtitle 360o video content will be presented. The results from the focus group shed light on how to address the new implications brought by immersive media in regard to subtitling. Some of the main areas discussed in the results are: subtitle position, contrast considerations for a correct reading, and how to indicate the location of the speakers, among others. Also, results show that users are willing to accept the implementation of new features in SDH in immersive content, such as icons for non-speech information or improvements to current standards
Tree-based Machine Learning Models with Applications in Insurance Frequency Modelling

As the insurance industry is highly data driven it is no surprise that machine learning (ML) has made its way into the industry. While GLMs are still the comfort zone of most actuaries, we have in ...
The effect of minute chronic release of hydrocarbon on soils of communities in proximity to oil fields

Objective: The study documents the effects of hydrocarbon exploitation (without major oil spills) on soils of a rural community, 5 Km from an E &P(exploration & production) site. Method/Statistical analysis: By 3 composite drill cuttings, 6 agricultural soil samples obtained from Oben oil field or oil mining lease 4 (OML 4) and Oben village, Nigeria were microbiologically examined. Water and crude oil samples were subjected to physical, chemical, and microbiological examinations. Pearson and Rosenberg model (1978) was used to describe changes in species richness and abundance from 2018– 2008. The 2008 environmental impact assessment report (EIA) was the environmental baseline. Finding: Significant changes in microbe diversity and population was noticed from 2008 to 2018 although no oil spills occurred. Total heterotrophic bacterial count (TBC) revealed greater diversity in 2008 than in 2018. The average TBC for soil samples from rural community and E&P site were 7.00.30 x 106 cfu/g, and 2.23 0.15102 cfu/g, respectively, in contrast to 6.60 x 10 9 cfu/g in 2008. Percentages of hydrocarbon utilizing microbes increased; indicative of a more favorable environment. Chi-square test (chi-square critical value = 9.49 & p-value= 0.05) used to compare observed population data with the expected; showed changes were not random chance but due to E &P activities. The prevailing regulatory approach was incapable of capturing fundamental issues of contemporary continuous, routine release of contaminants. Need to understand the subtle and pernicious effect of E&P operations on neighboring communities. Environmental laws should be reexamined. Applications: Microbes can be used to monitor minute, chronic release of hydrocarbon, inexpensively. They are “markers” in environmental changes even before major pollution, contamination, spillage or devastation occurs. Keywords: Acute contaminants on soils; contamination in nearby communities; oil field’s contaminants; contamination by proximity; chronic pollutants
Emotion Recognition from Blog Comments Based Automatically Generated Datasets and Ensemble Models

Emotions have a large influence on product loyalty and found that an individual have a positive emotional relationship with identified product, trust in the company and make purchases.The customer's decision is not founded on their facts/ information, but they rely on their emotions to get brand loyalty decisions.However, companies are struggling to adapt this strategy even with the huge amount of data they acquired, their utilization of this data as marketing influence and ongoing personalization tactics is limited due to the problem of analyzing voluminous data.This work offers new approach for automated categorization of emotions from textual data and was based on a sentence level utilizing the language translation machine and applying of learning machines and ensemble models.The experiment reveals that the proposed automatic emotion classifier performs well in classifying angry comments both in training and testing evaluation periods.Furthermore, the proposed emotion classifier produces a result of 75% accuracy under ensemble Naïve Bayes (NB) in the classification of sentence emotion during training period.Although during the actual testing period of the test data set, the proposed system was able to correctly classify 76% emotions of the sentences.Some of the reasons and causes for misclassification are analyzed and presented in this paper.
Progress in Superconducting Optoelectronic Networks for Neuromorphic Computing

We have proposed a superconducting opto-electronic platform for neuromorphic computing utilizing semiconductor light sources coupled to integrated waveguides for communication, and superconducting detectors and electronics for efficient computation. Here we summarize the recent experimental progress.
Photonic Recurrent Neural Networks with Gating Circuit

We experimentally demonstrate the first all-optical Recurrent-Neural-Network (RNN) with a gating circuit and a sigmoid activation unit. By introducing a gating circuit to the RNN we implemented the “forget” function, resembling an all-optical Gated-Recurrent-Unit (GRU).
A Dynamically Adding Information Recommendation System based on Deep Neural Networks

The recommendation system is a widely researched business tool that provides recommendations such as products or technologies that users are interested. The recommendation system learns user information by analyzing customer behavior and recommends products to meet customer needs. The existing mainstream recommendation systems still have room for improvement in terms of dynamically adding new users or new products to the system. The proposed method dynamically adds user and product information to the original recommendation system while recommending target content to new users or new products. While retaining the CIN and DNN structures, the networks associations are added to the before and after sequences. The input part of the add sequence is added to the previous sequence, the purpose is to dynamically update user and product information, adjust networks parameters based on sequence associations and learn high-order and low-order features of information. The results of comparative experiments show that our method could add new information dynamically with the low computing cost.
COVID-19 detected from targeted contact tracing, attempting to see the pattern in random happenings: early lessons in Malaysia.

The world feels strange as we face what is for most of us our first ever pandemic. The number of newly diagnosed cases rises daily in many parts of the world, and we are faced with the reality that there are still many things to learn about this new disease. We share here our experience of treating our first 199 COVID-19 patients in the Hospital Canselor Tuanku Muhriz, Pusat Perubatan Universiti Kebangsaan Malaysia (PPUKM).
A New Range Noise Perturbation Method based on Privacy Preserving Data Mining

With the rapid development of big data and data analysis technology, a large amount of data is collected and used for data mining. However, the prediction accuracy of data mining often fails to meet the needs of third-party data users, and holders may not explicitly and openly share their data. How to share data and handle data privacy issues between entities while ensuring error-free prediction becomes a challenging problem. Therefore, privacy preserving data mining(PPDM) technology was introduced. Among them, the random perturbation method is a direct and effective method to protect data privacy by modifying some sensitive attribute values. This paper proposes a new method of data mining for privacy protection, which is based on SVM machine learning algorithm for data mining and great balances the utility and security of data. JHI and WBC data sets in machine learning database were used in the experiment, and machine learning privacy protection parameters were used for security evaluation. Experiments were conducted to compare NMF and NMFSVD algorithms, and the experiments results show that RNP privacy protection data mining method not only has less error, but also can better protect the privacy of the data set.
Associated Hazard Assessment of IoT Vulnerability Based on Risk Matrix

With the rapid development of the technology, a large number of intelligent terminals access the IoT system. Attacks against the IoT system are increasing year by year. In view of the close connection between security vulnerabilities, in this paper, a method is proposed for hazard assessment of the IoT system using risk matrix, which can quantitatively analyze the security vulnerabilities of the system. Firstly, based on Common Vulnerability Scoring System 3.0, the global risk of vulnerability is calculated by the self-risk and the association risk. Secondly, we evaluate self-risk by vulnerability access difficulty degree and the vulnerability resources value. Then, the state attack graph is used in associated risk assessment, which can represent the relationship between vulnerabilities in the system and the subsequent nodes, and the probability of successful exploitation of the node is also considered.
Bayesian Nonparametric Bivariate Survival Regression for Current Status Data

We consider Bayesian nonparametric inference for event time distributions based on current status data. We show that under dependent censoring conventional mixture priors, including the popular Dirichlet process mixture prior, lead to biologically uninterpretable results as they unnaturally skew the probability mass for the event times toward the extremes of the observed data. Simple assumptions on dependent censoring can fix the problem. We then extend the discussion to bivariate current status data with partial ordering of the two outcomes. In addition to dependent censoring, we also exploit some minimal known structure relating the two event times. We design a Markov chain Monte Carlo algorithm for posterior simulation. Applied to a recurrent infection study, the method provides novel insights into how symptoms-related hospital visits are affected by covariates.
Capabilities of Cloud Services and Webinars Effectiveness of Teaching Humanities Students

espanolTeniendo en cuenta los problemas de eficiencia de la educacion de los estudiantes de humanidades, es imposible ignorar el uso de servicios en la nube como un fenomeno del entorno educativo de alta tecnologia, que se forma tanto dentro de la institucion, como de la region y el sistema educativo de paises individuales a escala global. El articulo confirma la relevancia del uso de la nube y seminarios web, considerando las caracteristicas de un entorno orientado a las nuevas tecnologias. Sobre la base de una encuesta de expertos se determino el nivel de aplicacion de las tecnologias en la nube y sus principales ventajas y limitaciones para garantizar la interactividad en los seminario web. EnglishConsidering the efficiency problems of the education of humanities students, it is impossible to ignore the use of cloud services as a phenomenon of the high-tech educational environment, which is formed both within the institution, and in the region of the education system of individual countries on a global scale. The article confirms the relevance of the use of the cloud and webinars, considering the characteristics of an environment oriented to new technologies. The level of application of cloud technologies and their main advantages and limitations to ensure interactivity in webinars were determined based on an expert survey
Formation of reflection on the basis of information and communication technologies

The article discusses the theoretical and methodological issues of professionaltraining, pedagogical technology of this process. The author describes the concept of training offuture teachers. A version of the model of the development of students’ cognitive interest based oninformation and communication technologies is presented. The characteristics of levels, criteria, andindicators of the development of students’ cognitive interest are given. The conclusions arepresented on the perspective modeling of the learning process considering the development ofcognitive interest based on information and communication technologies
Learning Big Gaussian Bayesian Networks: Partition, Estimation and Fusion

Structure learning of Bayesian networks has always been a challenging problem. Nowadays, massive-size networks with thousands or more of nodes but fewer samples frequently appear in many areas. We develop a divide-and-conquer framework, called partition-estimation-fusion (PEF), for structure learning of such big networks. The proposed method first partitions nodes into clusters, then learns a subgraph on each cluster of nodes, and finally fuses all learned subgraphs into one Bayesian network. The PEF method is designed in a flexible way so that any structure learning method may be used in the second step to learn a subgraph structure as either a DAG or a CPDAG. In the clustering step, we adapt the hierarchical clustering method to automatically choose a proper number of clusters. In the fusion step, we propose a novel hybrid method that sequentially add edges between subgraphs. Extensive numerical experiments demonstrate the competitive performance of our PEF method, in terms of both speed and accuracy compared to existing methods. Our method can improve the accuracy of structure learning by 20% or more, while reducing running time up to two orders-of-magnitude.
A Scalable Approach for Detecting Dumpsites using Automatic Target Recognition with Feature Selection and SVM through Satellite Imagery

Throughout the world, there is a great demand to map out the increasing environmental changes and life habitats on Earth. The vast majority of Earth Observations today, are collected using satellit ...
Using SQ3R to improve KPCE demonstration junior high students’ reading ability of expository texts

This study investigated how effective the SQ3R technique could be used in improving Kibi (KPCE) Demonstration Junior High students’ reading ability of expository texts. Using the simple random sampling, 45 students were selected to study the baseline performance of the students in reading comprehension, a pre-test was conducted after which twelve students were taught the intervention comprehension lessons using the SQ3R reading strategy.  A post-test was also conducted after the intervention to see whether there was an improvement in the performance of the students.  SPSS statistical software was used to calculate the mean value of both tests. The study discovered that p value p<0.05 and df=44, the ‘t’ value for the two means for matched groups was 6.7. This means that students’ performance in the post-test was much better than in the pre-test. This performance therefore was attributed to the intervention used. The study concluded that SQ3R is a better strategy for improving junior high students’ reading comprehension ability of expository texts; hence it should be encouraged in teaching comprehension.
Geometric Deep Learning Unlocks the Underlying Physics of Nanostructures

We present a learning-based technique to develop a reliable tool for discovering the underlying physics and feasibility of achieving different type of responses in electromagnetic nanostructures.
Weakly Supervised Content Selection for Improved Image Captioning

Image captioning involves identifying semantic concepts in the scene and describing them in fluent natural language. Recent approaches do not explicitly model the semantic concepts and train the model only for the end goal of caption generation. Such models lack interpretability and controllability, primarily due to sub-optimal content selection. We address this problem by breaking down the captioning task into two simpler, manageable and more controllable tasks -- skeleton prediction and skeleton-based caption generation. We approach the former as a weakly supervised task, using a simple off-the-shelf language syntax parser and avoiding the need for additional human annotations; the latter uses a supervised-learning approach. We investigate three methods of conditioning the caption on skeleton in the encoder, decoder and both. Our compositional model generates significantly better quality captions on out of domain test images, as judged by human annotators. Additionally, we demonstrate the cross-language effectiveness of the English skeleton to other languages including French, Italian, German, Spanish and Hindi. This compositional nature of captioning exhibits the potential of unpaired image captioning, thereby reducing the dependence on expensive image-caption pairs. Furthermore, we investigate the use of skeletons as a knob to control certain properties of the generated image caption, such as length, content, and gender expression.
A compact and inexpensive coherent Ising machine based on opto-electronic feedback for solving combinatorial optimization problems

We propose and demonstrate a new type of coherent Ising machine based on optoelectronic feedback systems for solving NP-hard optimization problems. The design is significantly cheaper and more compact than current state-of-the-art Ising machines.
Unifying Domain Adaptation into Simultaneous Discriminant Analysis: A Method for Low-Resolution Face Recognition with Single Sample Per Person

In some real-world applications, such as law enforcement, it is required to find out the true identity of an LR face image by using only the high-resolution (HR) profile face image. This leads to LR face recognition with single sample per person (SSPP). As it is intractable to find a match between LR and HR images directly and difficult to explore variations from SSPP, LR face recognition with SSPP is quite a challenging problem. To address the problem, based on simultaneously discriminative analysis (SDA), we propose to introduce an auxiliary dataset (source domain) containing multiple samples per person and integrate domain adaptation into the learning of coupled mappings. In the proposed method, the discriminative analysis of source domain and target domain, together with the domain adaptation between the source domain and target domain are unified into one framework, which produces Enhanced SDA. In this framework, the distribution mismatch is reduced, and the information in both the source domain and target domain are used to learn the coupled mappings. Enhanced SDA is extensively evaluated on LFW and SC face databases, the promising results of Enhanced SDA show its effectiveness in LR face recognition with single sample per person.
The teacher’s self-presentation in terms of students’ assessments or the study of the teacher’s self-presentation features

The article is devoted to the study of the phenomenon of self-presentation, as a significant mechanism that helps to build effective communication between teachers and students. To study the described phenomenon, we used the author's questionnaire "Assessment of the image of the teacher." A study conducted on a sample of teachers and their students led us to the conclusion that there are differences between the “I-image” of teachers and the “I-expert”, i.e. between teachers' self-image and student grading system. Teachers evaluate themselves higher in terms of the studied parameters than their students evaluate. As a result, we came to the empirical conclusion that it is important for teachers to pay attention to the development of self-presentation skills.
A Novel Approach for Extraction of Distinguishing Emotions for Semantic Granularity Level Sentiment Analysis in Multilingual Context

Introduction: Extraction of distinguishing semantic level emotions posed in multi-languages over social media is an essential task in the field of sentiment analysis or opinion mining. The extraction of emotions expressed in Dravidian or local languages combining with multi-languages over social media has become an essential challenge in the field of big data sentiment analysis. Methods: In the proposed approach, an innovative framework to recognize the sentiments of users in multi-languages or Dravidian languages text data using scientific linguistic theories has been defined. The proposed method used machine learning techniques such as naïve Bayes, support vector machine for fine-grained classification of multilingual text with help of lexicon-based features groups. Results: The results obtained by the experiments conducted on collected benchmark datasets in the proposed approach are outperformed and better in comparison with corpus-based and world level, phrase-level sentiment analysis for multilanguages text. Conclusion: Machine learning technnique SVM has outperformed for sentiment and emotion extraction.