Reviewerly/exHarmony huggingface dataset:
Relative Attribute Classification with Deep Rank SVM

Relative attributes indicate the strength of a particular attribute between image pairs. We introduce a deep Siamese network with rank SVM loss function, called Deep Rank SVM (DRSVM), in order to decide which one of a pair of images has a stronger presence of a specific attribute. The network is trained in an end-to-end fashion to jointly learn the visual features and the ranking function. We demonstrate the effectiveness of our approach against the state-of-the-art methods on four image benchmark datasets: LFW-10, PubFig, UTZap50K-lexi and UTZap50K-2 datasets. DRSVM surpasses state-of-art in terms of the average accuracy across attributes, on three of the four image benchmark datasets.
Chaotic Signatures and Global Solar Radiation model estimate over Nigeria, a Tropical region

Abstract. In a tropical region like Nigeria, accurate estimation and chaotic signatures of global solar radiation (Rs) are essential to the design of solar energy utilization systems in PV technology companies and one of the plant growth determinants in Agriculture. The Rs model is a function of solar declination angle, temperature difference, and relative humidity. In this paper, the daily re-analyzed atmospheric data obtained from the archive of ERA-Interim was used to estimate the nonlinear Global Solar radiation model and investigated chaotic signatures across the tropical climatic regions of Nigeria. The well-known statistical tools were used to analyze the chosen meteorological parameters and the correlation was found to be perfect, close with low values of RMSE across the selected regions over Nigeria. For proper modeling and prediction of the underlying dynamics, the extensive chaotic measures of phase space reconstruction using recurrence plots and recurrence quantification analyses are also presented, analyzed and discussed with the appropriate choice of embedded dimension, m, and time delay τ. The radiant energy from the sun is one of the most available and renewable resources across the season in a tropical region like Nigeria. The information, therefore, suggests how vital the solar irradiance can be useful in Agriculture and Photovoltaic technology companies. Based on the scarcely gauged of global solar radiation (GSR) at meteorological stations in developing countries. This demand necessitates a better understanding of the underlying dynamics for better prediction mostly by the nonlinear Global Solar radiation model estimate and chaotic signature measurement. The optimum usage of meteorological parameters such as solar radiation, relative humidity and temperature difference needs further studies, using RPs and RQA measures. However, several data such as rainfall data, geomagnetic data, ionospheric data, wind speed data etc obtained from different parts of the world have been estimated with several models and applied to RQA measures for better prediction and modeling. Using RPs and RQA, features due to external effects such as harmattan and intertropical discontinuity (ITD) on solar radiation data in this tropical region were uniquely identified. Meanwhile, the inverse characteristic behavior of solar radiation and relative humidity were vividly maintained. The results show a very low value of RMSE while the value of R2 is very closed to 1, which depicts a good prediction for all locations. However, the highest values of both SSE and RMSE, as well as the lowest value of R2 were observed in kano station, which indicates high solar irradiance location. The RPs reviewed the observed clusters points around the parallel diagonal lines with short segments, which implies the presence of chaos. Additional complex measure, the RQA also shows that the solar radiation during the dry season of the months has lower values of Lmax, determinism and entropy, and higher values during the wet season of the months.
New Models for Understanding and Reasoning about Speculative Execution Attacks

Spectre and Meltdown attacks and their variants exploit hardware performance optimization features to cause security breaches. Secret information is accessed and leaked through covert or side channels. New attack variants keep appearing and we do not have a systematic way to capture the critical characteristics of these attacks and evaluate why they succeed or fail. In this paper, we provide a new attack-graph model for reasoning about speculative execution attacks. We model attacks as ordered dependency graphs, and prove that a race condition between two nodes can occur if there is a missing dependency edge between them. We define a new concept, "security dependency", between a resource access and its prior authorization operation. We show that a missing security dependency is equivalent to a race condition between authorization and access, which is a root cause of speculative execution attacks. We show detailed examples of how our attack graph models the Spectre and Meltdown attacks, and is generalizable to all the attack variants published so far. This attack model is also very useful for identifying new attacks and for generalizing defense strategies. We identify several defense strategies with different performance-security tradeoffs. We show that the defenses proposed so far all fit under one of our defense strategies. We also explain how attack graphs can be constructed and point to this as promising future work for tool designers.
Tag and Correct: Question aware Open Information Extraction with Two-stage Decoding

Question Aware Open Information Extraction (Question aware Open IE) takes question and passage as inputs, outputting an answer tuple which contains a subject, a predicate, and one or more arguments. Each field of answer is a natural language word sequence and is extracted from the passage. The semi-structured answer has two advantages which are more readable and falsifiable compared to span answer. There are two approaches to solve this problem. One is an extractive method which extracts candidate answers from the passage with the Open IE model, and ranks them by matching with questions. It fully uses the passage information at the extraction step, but the extraction is independent to the question. The other one is the generative method which uses a sequence to sequence model to generate answers directly. It combines the question and passage as input at the same time, but it generates the answer from scratch, which does not use the facts that most of the answer words come from in the passage. To guide the generation by passage, we present a two-stage decoding model which contains a tagging decoder and a correction decoder. At the first stage, the tagging decoder will tag keywords from the passage. At the second stage, the correction decoder will generate answers based on tagged keywords. Our model could be trained end-to-end although it has two stages. Compared to previous generative models, we generate better answers by generating coarse to fine. We evaluate our model on WebAssertions (Yan et al., 2018) which is a Question aware Open IE dataset. Our model achieves a BLEU score of 59.32, which is better than previous generative methods.
The exhibition is just the beginning

Aim. The aim of this work is to present a qualitative analysis of the creative educational materials prepared by preservice teachers in the context of an educational experience around a museum exhibition about Science and Literature, described in a previous work (Authors, 2020). The responses to a final questionnaire about interdisciplinarity, integration of Sciences and Arts or the use of museums for children’s education are also analysed.&#x0D; Methods. The participants were 121 alumni of two different subjects “Natural Sciences for teachers” and “Literary training for teachers” at a Spanish university. Preservice teachers had to select an item of the museum exhibition and, as a group, prepare a catalogue intended for children, where they had to write a short text related to the object and activities for children in an exercise of didactic transposition. The students generated teaching materials with a high component of creativity and a multimodal approach, with a mixture of linguistic and non-linguistic codes. The qualitative analysis of these creative teaching materials was carried out with Atlas.ti v8. To assess the experience a questionnaire about different aspects of the proposal was completed by the students at the end of the semester.&#x0D; Results and conclusion. The analysis of the teaching materials demonstrates differences between the alumni attending both subjects in the type of items selected, the literary genre of the short text and the prepared activities. The responses to the final questionnaire give us an insight on the reasons for the item selection and show how most of the students considered the experience as very interesting and formative. We can conclude that the educational experience here described served as an exercise to internalise the benefits of this method and, at the same time, adapt it for their future students in Primary Education.
Determining Principal Component Cardinality through the Principle of Minimum Description Length

PCA (Principal Component Analysis) and its variants areubiquitous techniques for matrix dimension reduction and reduced-dimensionlatent-factor extraction. One significant challenge in using PCA, is thechoice of the number of principal components. The information-theoreticMDL (Minimum Description Length) principle gives objective compression-based criteria for model selection, but it is difficult to analytically applyits modern definition - NML (Normalized Maximum Likelihood) - to theproblem of PCA. This work shows a general reduction of NML prob-lems to lower-dimension problems. Applying this reduction, it boundsthe NML of PCA, by terms of the NML of linear regression, which areknown.
Why I'm not Answering

Safe deployment of deep learning systems in critical real world applications requires models to make few mistakes, and only under predictable circumstances. Development of such a model is not yet possible, in general. In this work, we address this problem with an abstaining classifier tuned to have $>$95\% accuracy, and identify the determinants of abstention with LIME (the Local Interpretable Model-agnostic Explanations method). Essentially, we are training our model to learn the attributes of pathology reports that are likely to lead to incorrect classifications, albeit at the cost of reduced sensitivity. We demonstrate our method in a multitask setting to classify cancer pathology reports from the NCI SEER cancer registries on six tasks of greatest importance. For these tasks, we reduce the classification error rate by factors of 2--5 by abstaining on 25--45\% of the reports. For the specific case of cancer site, we are able to identify metastasis and reports involving lymph nodes as responsible for many of the classification mistakes, and that the extent and types of mistakes vary systematically with cancer site (eg. breast, lung, and prostate). When combining across three of the tasks, our model classifies 50\% of the reports with an accuracy greater than 95\% for three of the six tasks and greater than 85\% for all six tasks on the retained samples. By using this information, we expect to define work flows that incorporate machine learning only in the areas where it is sufficiently robust and accurate, saving human attention to areas where it is required.
Asking Complex Questions with Multi-hop Answer-focused Reasoning

Asking questions from natural language text has attracted increasing attention recently, and several schemes have been proposed with promising results by asking the right question words and copy relevant words from the input to the question. However, most state-of-the-art methods focus on asking simple questions involving single-hop relations. In this paper, we propose a new task called multihop question generation that asks complex and semantically relevant questions by additionally discovering and modeling the multiple entities and their semantic relations given a collection of documents and the corresponding answer 1. To solve the problem, we propose multi-hop answer-focused reasoning on the grounded answer-centric entity graph to include different granularity levels of semantic information including the word-level and document-level semantics of the entities and their semantic relations. Through extensive experiments on the HOTPOTQA dataset, we demonstrate the superiority and effectiveness of our proposed model that serves as a baseline to motivate future work.
KETEPATAN PENGGUNAAN MEDIA PEMBELAJARAN UNTUK PEMBELAJARAN BAHASA INGGRIS PADA PELAKSANAAN TEACHING PRACTICE DI STBA PONTIANAK

This study aims to describe the accuracy of the learning media used by students when implementing teaching practices. The purpose of the implementation is to provide provisions to students before conducting internship. This research used a descriptive approach, using purposive sampling in selecting research subjects which adjust to the research objectives. The data collection technique used a classroom observation checklist with a guttman scale conducted by the observer. With the answer choices "yes" has a score of 1 and "no" has a score of 0. Material that is applied in the implementation of teaching practice is learning English for all levels of education. From the research results obtained from five groups based on predetermined learning methods, there are two groups of methods that fall into the category of "very appropriate" namely the silentway method and the SLT method. The TPR method group is included in the "appropriate" category. The lingual audio method and the SLT method group fall into the "inappropriate" category. This is because students are more focused on the application of learning methods, so students do not use learning media at all. It can be concluded that the role of learning media is so important, because it can display the visualization of an object. So that helps in increasing understanding, especially for the introduction of new vocabulary.
A fuzzy logic model for evaluating the standard performance of a prototype online voting system

This paper described the major challenges associated with existing methods of voting; hence a prototype online voting system was developed and proposed for credible election in Edo state with a mind set to trash out the various problems identified with the existing system. In order to determine if the prototype online voting system developed is of standard performance a fuzzy clustering means (FCM) was designed to evaluate and ascertain its performance based on certain criteria gathered using questionnaire designed. The FCM model was simulated and tested for evaluation taking into consideration stakeholders of election that were drawn from twelve (12) local government areas, out of the Eighteen (18) local government areas of Edo state. Opinions of stakeholders of the election concerning the wished-for model were arbitrarily sampled and analyzed for the use of assessment in particular when compared to the present system of selection. In addition, other factors that can promise an open and just election were also discussed and place into consideration throughout the implementation of the developed prototype online voting system. The result from the evaluation revealed that the seven (7) local government areas which formed about (58.33%) of the beyond least standard cluster and the five (5) local government areas, which also formed about (41.66%) of the regular standard cluster of the entire population of (12) local government areas were both above the average acceptable benchmark for elections, which is a key indicator that the developed prototype online voting software meets more than the standard for a credible election process and it is therefore proficient as a verdict announcer for a transparent electoral process when fully implemented and deployed for usage.
Video Surveillance System with Two Channels and Information Transmission

CCTV with recognition is widely used today. They are designed to provide information about the current state of a protected object (process) by collecting, processing, archiving, storing, displaying and analyzing this information. Modern video surveillance systems seek the outside world through the exchange of data via the Internet and allow not so much to observe the object and record the information received, but lately can process it with the use of image recognition algorithms and automatically affect the object management systems.The purpose of this work is to analyze the features of construction and operation of video surveillance systems, to consider various options for data transfer and the implementation of face recognition. Offer advanced CCTV options, such as additional face identification and emergency communications features.New CCTV systems are being built using IP cameras and related software. Smartphones and tablets, laptops, traditional computers and cloud technologies are also widely used as video viewing devices. Video surveillance is an important part of the security system, where IP cameras, recognition features and information transmission modules are now widely used in security systems.It is advisable to use an IP camera when there is constant Wi-Fi, which allows the information to be transmitted continuously. Surveillance cameras and human identification are important in that they can collect and store individuals' faces.The diversity of people's faces is enormous, which is why the system has a low probability of recognition. The requirements for the design and construction of such systems are incredibly high.Considering all the advantages and disadvantages of different types of data transmission modules, it follows that GSM is the best option for transmitting notifications, as its range is almost limitless. And the video stream can be transmitted both via GSM and via Wi-Fi.As a result of the analysis of modern autonomous CCTV systems, a variant of the system with additional functions is proposed. The system has two communication channels for better communication and identification.This system can be used in offices, establishments, and in ordinary homes. The proposed security system is available in facilities where an admission system is required. The system works as follows. For example, the room has a CCTV system and access is available to only three people. Their faces are already recorded in memory. When a person approaches the camera, the system identifies it and aligns it with the already stored information about the persons it has access to. If there is no match, a message is sent via the GSM module about the penetration of the object and a link to the IP address of the embedded server from which the video is broadcast from the premises.
Robust Person Re-Identification through Contextual Mutual Boosting

Person Re-Identification (Re-ID) has witnessed great advance, driven by the development of deep learning. However, modern person Re-ID is still challenged by background clutter, occlusion and large posture variation which are common in practice. Previous methods tackle these challenges by localizing pedestrians through external cues (e.g., pose estimation, human parsing) or attention mechanism, suffering from high computation cost and increased model complexity. In this paper, we propose the Contextual Mutual Boosting Network (CMBN). It localizes pedestrians and recalibrates features by effectively exploiting contextual information and statistical inference. Firstly, we construct two branches with a shared convolutional frontend to learn the foreground and background features respectively. By enabling interaction between these two branches, they boost the accuracy of the spatial localization mutually. Secondly, starting from a statistical perspective, we propose the Mask Generator that exploits the activation distribution of the transformation matrix for generating the static channel mask to the representations. The mask recalibrates the features to amplify the valuable characteristics and diminish the noise. Finally, we propose the Contextual-Detachment Strategy to optimize the two branches jointly and independently, which further enhances the localization precision. Experiments on the benchmarks demonstrate the superiority of the architecture compared the state-of-the-art.
Research Guides: Identity Theft: Finding Books

Guide for resources on Identity Theft which will provide an overview of Stetson library resources on the topic.
A Competence Based Curriculum in Teaching Financial Education-What Happens in Classrooms?

This paper looks at the transformative pedagogy-changing from Knowledge Based Curriculum (KBC) to Competence Based Curriculum (CBC) in lower secondary schools of Rwanda, which was adapted in 2016 by Rwanda Education Board (REB) through the Ministry of Education. The emphasis of this paper takes bias on financial education that the new competence based curriculum made it compulsory to lower secondary students, which is from senior one to senior three. The government of Rwanda made a significant effort to change the way the curriculum is taught into teaching and learning that equips learners with competent skills, values and attitudes suitable for this 21st century. Financial education involves imparting values, skills and attitudes through teaching and learning that enables the learner to easily visualise viable entrepreneurial decisions in daily life, allocating resources reasonably, making career trajectory quite easy, save for the future, manage available resources in our daily life, and many other areas such as having the culture of paying taxes, preparing business plans that are quite viable and feasible. In this paper, we will explore how the transition is, it is still demanding and little have changed from what ought to be, explore challenges that involves competence based pedagogy. This paper provides a critical review of the pedagogical practice of financial education, with an emphasis on the link between the theoretical practice and the ideal way of teaching and learning financial education. It being theoretical analysis, secondary data were used to analyse the challenges and possible strategies of transforming from theory to practice in the Rwandan schools.
Towards Interpretable Multi-Task Learning Using Bilevel Programming

Interpretable Multi-Task Learning can be expressed as learning a sparse graph of the task relationship based on the prediction performance of the learned models. Since many natural phenomenon exhibit sparse structures, enforcing sparsity on learned models reveals the underlying task relationship. Moreover, different sparsification degrees from a fully connected graph uncover various types of structures, like cliques, trees, lines, clusters or fully disconnected graphs. In this paper, we propose a bilevel formulation of multi-task learning that induces sparse graphs, thus, revealing the underlying task relationships, and an efficient method for its computation. We show empirically how the induced sparse graph improves the interpretability of the learned models and their relationship on synthetic and real data, without sacrificing generalization performance. Code at https://bit.ly/GraphGuidedMTL