Reviewerly/exHarmony huggingface dataset:
Artificial intelligence in the differentiation of tropical infections: A step ahead

Background: Tropical infectious diseases became an unavoidable major concern of today's world with a similarly presenting symptomatology and laboratory evaluation which makes difficult the early detection and distinct separation of infection. There is a need for the development of a tool that makes optimal use of distinct symptomatology amongst these infections and distinguishable laboratory parameters. So, we aimed to develop a decision-making tool for differentiating tropical infections such as Dengue, Malaria, Leptospirosis and Scrub typhus in a tertiary care hospital. Methods and materials: The study was conducted in Kasturba Hospital, Manipal after obtaining the ethical approval. A 9-item validated questionnaire was used to assess the need of physicians for the development of the decision-making tool and a case report form was designed accordingly. A total of 800 cases were collected with 200 cases each under individual disease category (Dengue, Malaria, Leptospirosis and Scrub typhus). The data set was divided into two portions for performing multinomial logistic regression analysis i.e., 170 x 4 cases training set and 30 x 4 cases validation set. Multinomial regression analysis was performed on the dataset followed by machine learning models are applied on the same dataset. In case of machine learning models, both multi-classification and binary classification models are applied accordingly. Results: The multinomial logistic regression analysis where dengue is taken as the reference category revealed a 60.7%, 62.5% and 66% of predictability for dengue, malaria and leptospirosis respectively which were relatively good. Whereas, scrub typhus showed only 39.5% of predictability score. Similarly, the multi-classification machine learning model showed 55–60% of predictability. On the other hand, binary classification machine learning algorithms showed a higher predictability score where 79–84% was observed with one Vs rest of disease and 69–88% was observed with one Vs one disease category, respectively. Conclusion: As of our knowledge, this is the first study on the application of machine learning in disease detection. Our study strongly recommends that machine learning techniques can be a very good tool in predicting tropical diseases and aid in early detection and better patient care.
Burn After Reading: A Shadow Stack with Microsecond-level Runtime Rerandomization for Protecting Return Addresses**Thanks to all the reviewers for their valuable comments. This research is supported by an Australian Research Council grant (DP180104069).

Return-oriented programming (ROP) is an effective code-reuse attack in which short code sequences (i.e., gadgets) ending in a ret instruction are found within existing binaries and then executed by taking control of the call stack. The shadow stack, control flow integrity (CFI) and code (re)randomization are three popular techniques for protecting programs against return address overwrites. However, existing runtime rerandomization techniques operate on concrete return addresses, requiring expensive pointer tracking. By adding one level of indirection, we introduce BarRA, the first shadow stack mechanism that applies continuous runtime rerandomization to abstract return addresses for protecting their corresponding concrete return addresses (protected also by CFI), thus avoiding expensive pointer tracking. As a nice side-effect, BarRA naturally combines the shadow stack, CFI and runtime rerandomization in the same framework. The key novelty of BarRA, however, is that once some abstract return addresses are leaked, BarRA will enforce the burn-after-reading property by rerandomizing the mapping from the abstract to the concrete return address space in the order of microseconds instead of seconds required for rerandomizing a concrete return address space. As a result, BarRA can be used as a superior replacement for the shadow stack, as demonstrated by comparing both using the 19 C/C++ benchmarks in SPEC CPU2006 (totalling 2,047,447 LOC) and analyzing a proof-of-concept attack, provided that we can tolerate some slight binary code size increases (by an average of 29.44%) and are willing to use 8MB of dedicated memory for holding up to 2 20 return addresses (on a 64-bit platform). Under an information leakage attack (for some return addresses), the shadow stack is always vulnerable but BarRA is significantly more resilient (by reducing an attacker's success rate to $\frac{1}{2^{20}}$ on average). In terms of the average performance overhead introduced, both are comparable: 6.09% (BarRA) vs. 5.38% (the shadow stack).
Modeling Attribution of Cyber Attacks Using Bayesian Belief Networks

The article makes an attempt to bring to the fore the various factors which are considered in the due process of attribution of a cyber-attack and the correlation of credible attribution with cyber deterrence. The focal point of the article is a three-step approach to model the decision-making process behind attribution of cyber-attacks using Bayesian Belief Networks and a case study to elucidate on the functioning of the model. Bayesian Belief Networks represent relationships between variables or multiple events and they are used to draw inference or estimate the probability to help decision-making under the conditions of uncertainty.
Profile of Student's Mathematical Representation Translation on the Verbal Problem

Translation among forms of representation defines as changing process one form of representation to other representations.Translation activities from one representation (source) to another (target) are very important in expressing mathematical ideas.In translating among representations, students are not only interacting with the basic concepts contained in the information, but also they must be able to connect parts of these basic concepts with different things related to the intended representation.The purpose of this qualitative research is: 1) to analyze the tendency of mathematical representations to be used in solving mathematical problems given in verbal representations, and 2) to analyze the translational structure of the target representations made.The research subjects were 3 rd year students of Mathematics Education at Universitas PGRI Semarang.The research data were taken by means of tests and interviews immediately after solving the math problems given in the form of verbal representations.The results showed that the group students who were solving the verbal questions indicated that they tended to translate from the verbal representations to symbolic representations in presenting definitions and in proof.However, this symbolic representation still contained verbal representations such as in the source problems.The stages of representation in this research followed the translation stages: unpacking the source, preliminary coordination, constructing the target, but left for determinant of equivalence.High-ability group of students in solving the questions tend to translate from verbal representation to symbolic representation.Further, some of them translate verbal to visual representation to increase understanding.The translation stages are unpacking the source, preliminary coordination, constructing symbol representation, constructing visual representation, determining the equivalence of the visual representation to verbal representation, and determining the equivalence of the symbol representation form to verbal representation.
What Phishing E-mails Reveal: An Exploratory Analysis of Phishing Attempts Using Text Analyzes

Phishers appear particularly interested in accounting and tax, with accountants and auditing firms as frequent targets because of the proximity to organizational resources. Since phishing is typically done using emails, we use text analysis to explore differences between phishing emails and other emails. Analyzing and comparing a database of phishing messages to a database of the Enron emails, we find that the phishing data is statistically significantly different across a large number of univariate text variable categories. Further, we generate a model of phishing as “power.” Using power as the dependent variable, independent variables of friend (who they pretend to be), achievement (of their goal), (to take your) money and (typically done at) work are used as a basis to estimate power in both the phishing and non-phishing messages and finds differences on the signs of the independent variables. Finally, using the output of a text analysis, we examine the ability of neural network models to differentiate between phishing emails and Enron emails, using size-matched samples.
Teaching and Learning in the Covid-19 Era: The Experience of an Italian Primary School Class

The authors present a case study, where digital technologies were implemented for online education (particularly, mathematics education) at Italian primary school level Results of this study confirm that the quick transition to the online form of education, due to the COVID-19 pandemic, was successful and provided experience which may be useful in future didactic activities
More or Less: When and How to Build Convolutional Neural Network Ensembles

Convolutional neural network models are applied to many tasks with rising complexity and data sizes. Researchers and practitioners seek to scale the representational power of these models by adding more parameters. Increasing parameters, though, requires additional critical resources such as memory and compute time leading to increased training and inference cost. Thus a consistent challenge is to obtain as high as possible accuracy within a parameter budget. As neural network designers navigate this complex landscape, they are guided by conventional wisdom. This comes from empirical studies of the design space. We identify a critical part of this design space that is not well-understood: That is how to decide between the alternatives of expanding a single convolutional network model or increasing the number of networks and using them together in an ensemble. We study this question in detail through extensive experimentation across various network architectures and data sets. Crucially, we provide a robust assessment, missing in previous studies, by fixing the number of parameters. We also consider a holistic set of metrics such as training time, inference time, and memory usage. Contrary to conventional wisdom, we show that when we perform a holistic and robust assessment, we uncover a wide design space, where ensembles not only provide better accuracy but also train faster, and deploy at a speed comparable to single convolutional networks with the same total number of parameters.
Single Image Dehazing Using Non Local Atmospheric Light Estimation

The presence of dust and microscopic suspended particles in the atmosphere cause low contrast and deterioration of subtle details in images. These lost features pose significant challenges on applications, relying on images as input, e.g. satellite imaging, medical field, and remote sensing. Thus, dehazing becomes a crucial task. The process of dehazing should preserve the structure and the overall color property of the image. Most of the dehazing algorithms developed have statistical monitoring of outdoor images, which use prior based knowledge. They prove inefficient in real-world scenarios because they assume the haze is uniformly distributed across the entire space. The paper presents an algorithm to estimate the airlight using the atmospheric model of image formation, incorporating the atmospheric light and transmission map to refine an image. The atmospheric light is calculated by segmentation using linear spectral clustering and then an improved quadtree algorithm. The atmospheric light uses the concept of haze lines to produce a novel transition map obtained from our algorithm. The comparison of our method shows the superiority of the proposed method.
A Music Recommendation System based on Reinforcement Learning

At present, collaborative filtering recommendation method is the most widely used method. However, there are still two key problems in recommender system: cold start, exploration and exploitation. This paper uses multi-armed bandits (ϵ-greedy) algorithm in reinforcement learning to solve the problem of cold start and exploration-exploitation trade-off problem in music recommendation system. Experimental results show that compared with the traditional music recommendation method based on singular value decomposition, the music recommendation system based on this method can better meet the personalized needs of users.
Research on Monitoring and Diagnosis Technology of Data Anomaly in Distribution Network

Abstract Aiming at the problems of a large amount of abnormal data in the distribution network and the poor adaptability of traditional distribution network data, the paper proposes an intelligent distribution network status and abnormal data monitoring method, which has undergone data pre-processing and data fusion, Data analysis and visualization, and state identification and processing, a total of 4 links, turning multiple electrical feature quantities into a single comprehensive feature quantity, monitoring the operation status of the distribution network, and according to the relationship between each node and the size of the local anomaly factor to achieve intelligence Judgment and location of distribution network fault areas. The thesis realizes the detection and location of faults according to the size of each node's LOF value and the node's association relationship. After RTDS semi-physical closed-loop test, the accuracy and reliability of fault determination and location are high, which has certain reference value.
Hybrid Cascade Point Search Network for High Precision Bar Chart Component Detection

Bar charts are commonly used for data visualization. One common form of chart distribution is in its image form. To enable machine comprehension of chart images, precise detection of chart components in chart images is a critical step. Existing image object detection methods do not perform well in chart component detection which requires high boundary detection precision. And traditional rule-based approaches lack enough generalization ability. In order to address this problem, we design a novel two-stage component detection framework for bar charts that combines point-based and region-based ideas, by simulating the process that human creating bounding boxes for objects. The experiment on our labeled ChartDet dataset shows our method greatly improves the performance of chart object detection. We further extend our method to a general object detection task and get comparable performance.
خود-کارآمدی فردی و کارآمدی گروهی معلم به عنوان پیش‌بینی‌کننده‌های تعهد شغلی معلم: بررسی موردی معلمان زبان انگلیسی ایرانی

As a result of the role of teachers’ psychological factors in affecting their performance, there has been a bourgeoning interest in the significance of teachers' constructs in influencing their effectiveness and work engagement. As an attempt to shed more light on the relationships between teacher psychological factors in English as a Foreign Language (EFL) context, the purpose of this study was set to explore the role of teachers’ self-efficacy and their collective efficacy as the predictors of work engagement among Iranian EFL teachers. In so doing, a sample of 168 English teachers completed the self-report scales of the constructs under investigation. Structural Equation Modeling was adopted to test the structural model of the constructs. The results revealed that teachers’ sense of efficacy explained for 23.7 % of the work engagement variance whereas teacher collective efficacy accounted for 10.5 % of the variance in work engagement. It was revealed that although each variable had a unique contribution to work engagement, teachers’ individual self-efficacy was a more powerful predictor of work engagement than their collective efficacy. Based on the findings, important implications can be provided for EFL teacher education programs.
Measuring phonological complexity in West African languages

West Africa as an area of linguistic diversity and unification processesWest Africa as a sub-region of the African continent is defi ned mostly on geographic and political criteria which exclude Northern Africa and the Maghreb covering the Sub-Saharan countries from Senegal to Nigeria.As a region of linguistic studies, West Africa adheres to these limits, though genetic relationship and historical contacts between languages make these conventional boundaries vague in a number of respects.The region is characterized by linguistic diversity which determines the prominence of research oriented at multilingualism and language contact.The works conducted so far has focused on identifying convergence zones rather than providing the proof of the linguistic coherence in the entire region.The term convergence zone refers to a region where many linguistic features are shared across the language boundaries.The two largest units, i.e.Macro-Sudan Belt extending from Senegal to Ethiopia (Güldemann 2008) and Wider Lake Chad Region overlap to some extent, especially in Nigeria where genetically distinct and structurally diff erent languages meet (Ziegelmeyer 2015;Wolff & Löhr 2005;Zima 2009; Cyff er & Ziegelmeyer 2009).West Africa is characterized by extensive societal multilingualism (Lüpke & Chambers 2010).Along with indigenous languages superimposed foreign languages such as French, English or Portuguese are used.The region has always been an area where languages brought by scholars, traders or travelers were in constant confrontation with those used locally.Muslim teachers and traders moving along West Africa brought Arabic to this region.The emergence of political centers such as Ghana in 12 th century, Mali in 14 th century, Songhay in 15 th century or the Sokoto Caliphate in 19 th century strengthened the position of Arabic as the language of courts, written correspondence, religious and legal teaching.Arabic also became an important contact language among educated and infl uential people living in towns and it had an impact on the major languages of the Sahel and northern savannah.The historical empires and city-states also promoted languages spoken by the ruling class such as Hausa, Fulfulde or Mande languages, pushing many other local languages aside.Due to globalization, urbanization and economic development, the number of languages
Challenges in Cyber Security Education

Cyber security education is an important and pertinent topic as it plays a major role in mitigating the risks caused by a global shortage of cyber security experts. In order to better support this crucial function, a cyber security skills framework needs to be agreed upon by academics in this ﬁeld, along with an increase in the visibility of cyber security education and training. Without these, there is likely to be a long-term shortfall between the number of skilled cyber security professionals and demand, potentially leaving organisations, institutions, and governments vulnerable. Our contribution to this challenge is threefold. Firstly this article addresses the development of a cyber security skills framework and its applicability. Secondly, we have analysed a sample of 87 study programs in order to produce an overview of cyber security topic areas. Moreover, the collected data are visualized in an interactive map, i.e. a dynamic web application with the aim to help students in their search for a cyber security study program. Finally, a cyber security curricula designer tool is proposed; this application would serve as a guideline for cyber security curricula designers.
COMBATTING CYBERCRIMES IN THE EDUCATION SECTOR

Several computer related crimes known as cybercrimes are committed daily such as fake bank alert messages (SMS)/unsolicited SMS requesting details as Bank Verification Number (BVN), Advance fee fraud (Yahoo Yahoo), identity theft, piracy, pornography, hacking, forgery such as fake certificates etc.Thus, People connected to the internet through their computers/mobile phones have experienced such crimes.Therefore, the increasing rate of cybercrimes in our societies today, is a critical concern and an imperative area of research study.Hence, the impact of these crimes can be felt severely on a nation and it economy at large.We have propose a new approach that focuses on the prominent cybercrimes in Nigeria and presents a brief analysis of cybercrimes within the Nigerian tertiary institutions.And finally highlights methods of Cybercrime detection and prevention in order to efficiently combat cybercrimes.
Construction and Application of Fuzzy Evaluation Index System for Classroom Teaching Quality of Applied Undergraduate

Based on the research and achievements of collegiate classroom teaching quality evaluation at home and abroad, combined with AHP fuzzy evaluation theory, this paper constructed the applied undergraduate classroom teaching evaluation index system by using AHP, and applied it to the comprehensive evaluation of the applied undergraduate classroom teaching quality in Chongqing A college. The research results basically verified the effectiveness and feasibility of the evaluation index system, which has positive reference significance for the continuous improvement of the applied undergraduate classroom teaching quality evaluation of Chongqing A college and the teaching quality evaluation of similar colleges.
COVID-19 FACE MASK DETECTOR

COVID-19 or popularly known as Corona Virus has changed and disrupted our lives to such an extent that we humans are forced to lock ourselves inside the home and avoid any physical connection with someone else.During this phase we got to know the importance of Face Mask and Hand sanitizers that we usually neglect in our daily lives.We now avoid a person who is not wearing a face mask as it can cause you Covid-19.But sometimes it is very difficult for a human to detect out of many people, who is wearing mask or not.Thus I built a Computer Vision model that detects whether a person is wearing a mask or not using OpenCV and TensorFlow/Keras.
Developing a Clustering Structure with Consideration of Cross-Domain Text Classification based on Deep Sparse Auto-encoder

Abstract feature dimension and cross-domain classification in text classification may lead to low efficiency of text classification. Therefore, in order to study the specificity of the 2019-novel coronavirus, this paper proposes an improved clustering structure based on deep sparse auto-encoder for cross-domain text classification. In this structure, the word vector model and cosine similarity are used to construct the similarity matrix, and then the deep sparse automatic encoder based on unsupervised learning is used to reduce the dimension and extract the feature structure of complex network, and the k-means clustering method is used for testing. Finally, the results are obtained through mean-shift autonomous classification. The performance of the structure is verified on the data set of the title of the paper. The experimental results show the effectiveness of the structure and the practicability of the paper.