Reviewerly/exHarmony huggingface dataset:
Blind Backdoors in Deep Learning Models

We investigate a new method for injecting backdoors into machine learning models, based compromising the loss-value computation in the model-training code. We use it to demonstrate new classes of backdoors strictly more powerful than those in the prior literature: single-pixel and physical backdoors in ImageNet models, backdoors that switch the model to a covert, privacy-violating task, and backdoors that do not require inference-time input modifications. Our attack is blind: the attacker cannot modify the training data, nor observe the execution of his code, nor access the resulting model. The attack code creates poisoned training inputs on the fly, as the model is training, and uses multi-objective optimization to achieve high accuracy both the main and backdoor tasks. We show how a blind attack can evade any known defense and propose new ones.
PERIDOT: Modeling Execution Time of Spark Applications

A data analytics application submitted to a Spark cluster often has to finish executing by a specified deadline. To use cluster resources effectively, the key challenge is having the ability to gain quick insights on how the execution time of any given application is likely to be impacted by the resources allocated to the application, e.g., the number of Spark executor cores and the size of the input data. Such insights can be used to quickly estimate the required resources needed for the desired execution time. Our paper proposes an automated execution time estimation approach called PERIDOT that involves executing a given application under a fixed resource setting with two small subsets of its input data to offer fast, lightweight execution time predictions. It analyzes these two executions to estimate the internal dependencies of the application and combines them with knowledge of Sparks data partitioning mechanisms to derive an analytic model that can estimate execution times for other resource settings and input data sizes. Our results from a wide range of applications and multiple Spark clusters show that PERIDOT can accurately estimate the execution time of an application from limited historical data, and suggest the minimum amount of resources required to meet an execution deadline.
The Effectiveness Of Employing Telegram Application In Teaching Vocabulary: A Quasi Experimental Study

Technology usage has become a crucial factor in learning English in and out of the classroom. Nowadays, English classroom utilizing digital technology application. Digital technologies have been utilized to improve and help to teach and learn the language. Digital technologies help educators to adopt modern teaching methods, therefore, improve the process of teaching and learning the English language. This paper investigates the effect of using digital technology(Telegram) on EFL students’ vocabulary learning. 40 Arab high school pre-intermediate EFL students at Arabic International School in Kuala Lumpur were selected. Using a quasi-experimental design, the data was collected through a placement test, pre-post-test. Students were divided into control and experimental groups. The experimental group received their lessons through Telegram. However, the control group practised conventional teaching methods. The data of the experimental post-test showed that Telegram is an effective teaching tool that motivates students to learn vocabulary enjoyably. Implications and recommendations are drawn at the end of the paper.
Hadamard’s Defense Against Adversarial Examples

Adversarial images have become an increasing concern in real-world image recognition applications with deep neural networks (DNN).We observed that all the architectures in DNN use onehot encoding after a softmax layer.The attacker can make minute modifications in the adversarial example rendering those changes imperceptible to a human observer.The Hamming distance between pairs of onehot codes is two, independent of the number of classes, and we gather this is at the heart of a successful attack.This paper proposes increasing the Hamming distance between class codes as a generic defense against adversarial attacks from the above observation.It is only natural to use error-correcting codes for this task.We present an experimental comparison of our proposal against SOTA adversarial defenses.We conducted tests for six types of white-box attacks and one black-box scenario described in the literature.In all the experiments, our proposal's precision surpasses the state-of-art defenses by a wide margin.
Bidirectional Relationship Inferring Network for Referring Image Localization and Segmentation

Recently, referring image localization and segmentation has aroused widespread interest. However, the existing methods lack a clear description of the interdependence between language and vision. To this end, we present a bidirectional relationship inferring network (BRINet) to effectively address the challenging tasks. Specifically, we first employ a vision-guided linguistic attention module to perceive the keywords corresponding to each image region. Then, language-guided visual attention adopts the learned adaptive language to guide the update of the visual features. Together, they form a bidirectional cross-modal attention module (BCAM) to achieve the mutual guidance between language and vision. They can help the network align the cross-modal features better. Based on the vanilla language-guided visual attention, we further design an asymmetric language-guided visual attention, which significantly reduces the computational cost by modeling the relationship between each pixel and each pooled subregion. In addition, a segmentation-guided bottom-up augmentation module (SBAM) is utilized to selectively combine multilevel information flow for object localization. Experiments show that our method outperforms other state-of-the-art methods on three referring image localization datasets and four referring image segmentation datasets.
Model-Agnostic Local Explanations with Genetic Algorithms for Text Classification

The interpretability of black-box text classification models has been receiving widespread attention in recent years accompanying the growing popularity of artificial intelligence.To garner user trust on the model's decision-making process, it is imperative to provide faithful instance-wise justifications and rationalize the prediction in a human-readable way.In this paper, we address this challenge by introducing Locally Universal Rules (LURs) as model-agnostic local explanations.LURs are a subset of input words sufficient for the model to arrive at a particular prediction, even if the rest of words are perturbed slightly.We show the identification of the optimal LUR is NP-complete.Consequently, we propose a population-based algorithm LUR-Locator to perform the constrained optimization efficiently.We conduct extensive experiments to evaluate our algorithm on a cross product of well-established text classification datasets and models.The empirical results demonstrate that LURLocator can efficiently generate high-quality local explanations, as compared to existing explanatory methods.
Do Acoustic Word Embeddings Capture Phonological Similarity? An Empirical Study

Several variants of deep neural networks have been successfully employed for building parametric models that project variable-duration spoken word segments onto fixed-size vector representations, or acoustic word embeddings (AWEs). However, it remains unclear to what degree we can rely on the distance in the emerging AWE space as an estimate of word-form similarity. In this paper, we ask: does the distance in the acoustic embedding space correlate with phonological dissimilarity? To answer this question, we empirically investigate the performance of supervised approaches for AWEs with different neural architectures and learning objectives. We train AWE models in controlled settings for two languages (German and Czech) and evaluate the embeddings on two tasks: word discrimination and phonological similarity. Our experiments show that (1) the distance in the embedding space in the best cases only moderately correlates with phonological distance, and (2) improving the performance on the word discrimination task does not necessarily yield models that better reflect word phonological similarity. Our findings highlight the necessity to rethink the current intrinsic evaluations for AWEs.
Voting Over a Distributed Ledger: An Interdisciplinary Perspective

This work discusses the potential of a blockchain based infrastructure for a decentralised online voting platform. When compared to monograph based voting, online voting can vastly increase the speed that votes can be counted, expand the overall accessibility of the election system and decrease the cost of turnout. Yet despite these advantages, online voting for political office is subject to fraud at various levels due to its centralised nature. In this monograph, we describe a general architecture of a centralised online voting system and detail which areas of such a system are vulnerable to electoral fraud. We then proceed to introduce the key ideas underlying blockchain technology as a decentralised mechanism that can address these problems. We discuss the advantages and weaknesses of the blockchain technology, the protocols the technology uses and what criteria a good blockchain protocol should satisfy (depending on the voting application). We argue that the decentralisation inherent in the blockchain technology could increase the public's trust in national elections, as well as eliminate voter impersonation and double voting. We conclude with a discussion regarding how economists and social scientists can collaborate with the blockchain community in a research agenda on the design of efficient blockchain protocols and new voting systems such as liquid democracy.
Conservative Policy Construction Using Variational Autoencoders for Logged Data With Missing Values

In high-stakes applications of data-driven decision-making such as healthcare, it is of paramount importance to learn a policy that maximizes the reward while avoiding potentially dangerous actions when there is uncertainty. There are two main challenges usually associated with this problem. First, learning through online exploration is not possible due to the critical nature of such applications. Therefore, we need to resort to observational datasets with no counterfactuals. Second, such datasets are usually imperfect, additionally cursed with missing values in the attributes of features. In this article, we consider the problem of constructing personalized policies using logged data when there are missing values in the attributes of features in both training and test data. The goal is to recommend an action (treatment) when <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\tilde { \boldsymbol {X}}$ </tex-math></inline-formula> , a degraded version of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\boldsymbol {X}$ </tex-math></inline-formula> with missing values, is observed. We consider three strategies for dealing with missingness. In particular, we introduce the conservative strategy where the policy is designed to safely handle the uncertainty due to missingness. In order to implement this strategy, we need to estimate posterior distribution <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$p(\boldsymbol {X}| \tilde { \boldsymbol {X}})$ </tex-math></inline-formula> and use a variational autoencoder to achieve this. In particular, our method is based on partial variational autoencoders (PVAEs) that are designed to capture the underlying structure of features with missing values.
Deep Learning-Enabled Multitask System for Exercise Recognition and Counting

Exercise is a prevailing topic in modern society as more people are pursuing a healthy lifestyle. Physical activities provide significant benefits to human well-being from the inside out. Human pose estimation, action recognition and repetitive counting fields developed rapidly in the past several years. However, few works combined them together to assist people in exercise. In this paper, we propose a multitask system covering the three domains. Different from existing methods, heatmaps, which are the byproducts of 2D human pose estimation models, are adopted for exercise recognition and counting. Recent heatmap processing methods have been proven effective in extracting dynamic body pose information. Inspired by this, we propose a deep-learning multitask model of exercise recognition and repetition counting. To the best of our knowledge, this approach is attempted for the first time. To meet the needs of the multitask model, we create a new dataset Rep-Penn with action, counting and speed labels. Our multitask system can estimate human pose, identify physical activities and count repeated motions. We achieved 95.69% accuracy in exercise recognition on the Rep-Penn dataset. The multitask model also performed well in repetitive counting with 0.004 Mean Average Error (MAE) and 0.997 Off-By-One (OBO) accuracy on the Rep-Penn dataset. Compared with existing frameworks, our method obtained state-of-the-art results.
A Flexible Input Mapping System for Next-Generation Virtual Reality Controllers

This paper proposes an input mapping system that can transform various input signals from next-generation virtual reality devices to suit existing virtual reality content. Existing interactions of virtual reality content are developed based on input values for standardized commercial haptic controllers. This prevents the challenge of new ideas in content. However, controllers that are not compatible with existing virtual reality content have to take significant risks until commercialization. The proposed system allows content developers to map streams of new input devices to standard input events for use in existing content. This allows the reuse of code from existing content, even with new devices, effectively reducing development tasks. Further, it is possible to define a new input method from the perspective of content instead of the sensing results of the input device, allowing for content-specific standardization in content-oriented industries such as games and virtual reality.
Sentiment Analysis of Urdu Language on different Social Media Platforms using Word2vec and LSTM

Sentiment analysis is the process to analyze the opinions, emotions or sentiments regarding areview, comment, organization, firms or some event etc. with the introduction of social mediapeople tend to share their sentiments, opinions, emotions and ideas through them. Urdu which isa dominant language in Indian sub-content. Most of people tend to share their sentiments usingUrdu as one of main language on these social media sites. In this paper the Urdu text availableon different social media platformswill be distributed into their vector forms by using theWord2vec model and Long Short-Term Memory Units will be utilized for text classification andSoftMaxfunction will be used as an activation function in LSTM. This SoftMax function hasbeen used for creating sentence polarity of positive, negative or neutral attribute. In this researchwork the whole process has been used with recurrent Neural network.
Effectiveness of Methods and Tools used for Collection of Roman Urdu and English Multilingual Corpus

Corpus or data acquisition is a critical step in the development of any system for natural language processing. The collection of corpora aids in the development of more precise systems. Collecting a mixed multilingual corpus is a time-consuming and difficult process that yields a limited amount of data. Only mixed Roman Urdu and English multilingual corpora are collected in the research work. Tweepy, Phantom JS, Selenium, Urlib and Amazon Mechanical Turk have been used to collect the mixed multilingual Roman Urdu and English corpus. Results showed the collected text is good quality corpus.
ALFO: Adaptive Light Field Over-Segmentation

Automatic image over-segmentation into superpixels has attracted increasing attention from researchers to apply it as a pre-processing step for several computer vision applications. In 4D Light Field (LF) imaging, image over-segmentation aims at achieving not only superpixel compactness and accuracy but also cross-view consistency. Due to the high dimensionality of 4D LF images, depth information can be estimated and exploited during the over-segmentation along with spatial and visual appearance features. However, balancing between several hybrid features to generate robust superpixels for different 4D LF images is challenging and not adequately solved in existing solutions. In this paper, an automatic, adaptive, and view-consistent LF over-segmentation method based on normalized LF cues and K-means clustering is proposed. Initially, disparity maps for all LF views are estimated entirely to improve superpixel accuracy and consistency. Afterwards, by using K-means clustering, a 4D LF image is iteratively divided into regular superpixels that adhere to object boundaries and ensure cross-view consistency. Our proposed method can automatically adjust the clustering weights of the various features that characterize each superpixel based on the image content. Quantitative and qualitative results on several 4D LF datasets demonstrate outperforming performance of the proposed method in terms of superpixel accuracy, shape regularity and view consistency when using adaptive clustering weights, compared to the state-of-the-art 4D LF over-segmentation methods.
Factors affecting university image among undergraduate students: the case study of qatar university

Formulae display:?Mathematical formulae have been encoded as MathML and are displayed in this HTML version using MathJax in order to improve their display. Uncheck the box to turn MathJax off. This feature requires Javascript. Click on a formula to zoom.
Transfer Learning Approach - An Efficient Method to Predict Rainfall Based on Ground-Based Cloud Images

Clouds play a vital role in climate prediction. Rainfall prediction also majorly depends on the status and types of clouds present in the sky. Therefore, cloud identification is the most exciting and vital topic in meteorology and attracts most researchers from other areas. This paper presents the transfer learning technique to predict the Rainfall based on ground-based Cloud images responsible for rains. It will predict the estimated Rainfall by identifying the type of cloud by taking cloud images as input. The cloud images in the dataset are divided into three categories(classes) labeled as no-rain to very low-rain, low to medium-rain, and medium to high Rain based on the associated Precipitation responsible for the appropriate Rainfall. This model will be most helpful to the farmers to manage their Irrigation by knowing the status of Rainfall before every irrigation cycle or can also be helpful to take decisions on the outdoor events by taking prior knowledge of Rain. The model is trained on three classes to predict the Rainfall and firstly experimented with CNN. To improve the performance, the experiment is carried out with some best-pretrained models VGG16, Inception-V3, and XCeption using transfer learning and, the results are compared to the regular CNN model. The transfer learning technique is outperformed to get good accuracy as the dataset is too small and presented the best possible results of the model. Google colab with GPU setting makes the task fast and efficient to get the appropriate results in time, and performance achieved by transfer learning is excellent and can fulfill real-time requirements.
An Intelligent Blockchain and Software-Defined Networking-Based Evidence Collection Architecture for Cloud Environment

Cloud forensics is an extension of contemporary forensic science that guards against cybercriminals. However, consolidated data assortment and storage compromise the legitimacy of digital indication. This essay proposes an evolving modern algorithm automated forensic platform based on the blockchain idea. This proposes forensic structure design, evidence gathering, and storage on a blockchain that are peer to peer. Secure Block Verification Mechanism (SBVM) will protect unauthorised users. Secret keys are optimally produced using the cuckoo search optimization method. All data are saved and encrypted at the cloud authentication server for secrecy. Confidentiality-Based Algebraically Homomorphism, a new encryption method, is given to cryptosystem learning. Every data is assigned a block in the SDN controller, and the history is kept as metadata about data. Each block has a Secure Hash Algorithm version 3 of 512-bit hash-based tree. Our approach uses graph theory-based graph neural networks in Smart Contracts to track users’ data (GNNSC). Finally, a blockchain-based evidence graph allows for evidence analysis. The experiments were run in a cloud environment with Python and network simulator-3.30 (for software-defined network). We achieved good results in terms of evidence response time, cloud evidence insertion time, cloud evidence verification time, computational overhead, hash calculation time, key generation times, and entire overall change rate of indication using our newly deliberated forensic construction using blockchain (FAuB).
Evaluation of Feature Selection Methods for Mammographic Breast Cancer Diagnosis in a Unified Framework

Over recent years, feature selection (FS) has gained more attention in intelligent diagnosis. This study is aimed at evaluating FS methods in a unified framework for mammographic breast cancer diagnosis. After FS methods generated rank lists according to feature importance, the framework added features incrementally as the input of random forest which performed as the classifier for breast lesion classification. In this study, 10 FS methods were evaluated and the digital database for screening mammography (1104 benign and 980 malignant lesions) was analyzed. The classification performance was quantified with the area under the curve (AUC), and accuracy, sensitivity, and specificity were also considered. Experimental results suggested that both infinite latent FS method (AUC, <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mn>0.866</mn> <mo>±</mo> <mn>0.028</mn> </math> ) and RELIEFF (AUC, <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"> <mn>0.855</mn> <mo>±</mo> <mn>0.020</mn> </math> ) achieved good prediction ( <math xmlns="http://www.w3.org/1998/Math/MathML" id="M3"> <mtext>AUC</mtext> <mo>≥</mo> <mn>0.85</mn> </math> ) when 6 features were used, followed by correlation-based FS method (AUC, <math xmlns="http://www.w3.org/1998/Math/MathML" id="M4"> <mn>0.867</mn> <mo>±</mo> <mn>0.023</mn> </math> ) using 7 features and WILCOXON (AUC, <math xmlns="http://www.w3.org/1998/Math/MathML" id="M5"> <mn>0.887</mn> <mo>±</mo> <mn>0.019</mn> </math> ) using 8 features. The reliability of the diagnosis models was also verified, indicating that correlation-based FS method was generally superior over other methods. Identification of discriminative features among high-throughput ones remains an unavoidable challenge in intelligent diagnosis, and extra efforts should be made toward accurate and efficient feature selection.