Reviewerly/exHarmony huggingface dataset:
Detecting Software Security Vulnerabilities Via Requirements Dependency Analysis

Cyber attacks targeting software applications have a tremendous impact on our daily life. For example, attackers have utilized vulnerabilities of web applications to steal and gain unauthorized use of sensitive data stored in these systems. Previous studies indicate that security testing is highly precise, and therefore is widely applied to validate individual security requirements. However, dependencies between security requirements may cause additional vulnerabilities. Manual dependency detection faces scalability challenges, e.g., a previous study shows that the pairwise dependency analysis of 40 requirements would take around 12 hours. In this article, we present a novel approach which integrates the interdependency among high-level security requirements, such as those documented in policies, regulations, and standards. We then use automated requirements tracing methods to identify product-level security requirements and their dependencies. Our manual analysis of HIPAA and FIPS 200 leads to the identification of five types of high-level security requirements dependencies, which further inform the automated tracing methods and guide the designs of system-level security tests. Experimental results on five projects in healthcare and education domains show the significant recall improvements at 81 percent. Our case study on a deployed production system uncovers four previously unknown vulnerabilities by using the detected requirements dependencies as test paths, demonstrating our approach's value in connecting requirements engineering with security testing.
Sample-Efficient Neural Architecture Search by Learning Actions for Monte Carlo Tree Search

Neural Architecture Search (NAS) has emerged as a promising technique for automatic neural network design. However, existing MCTS based NAS approaches often utilize manually designed action space, which is not directly related to the performance metric to be optimized (e.g., accuracy), leading to sample-inefficient explorations of architectures. To improve the sample efficiency, this paper proposes Latent Action Neural Architecture Search (LaNAS), which learns actions to recursively partition the search space into good or bad regions that contain networks with similar performance metrics. During the search phase, as different action sequences lead to regions with different performance, the search efficiency can be significantly improved by biasing towards the good regions. On three NAS tasks, empirical results demonstrate that LaNAS is at least an order more sample efficient than baseline methods including evolutionary algorithms, Bayesian optimizations, and random search. When applied in practice, both one-shot and regular LaNAS consistently outperform existing results. Particularly, LaNAS achieves 99.0 percent accuracy on CIFAR-10 and 80.8 percent top1 accuracy at 600 MFLOPS on ImageNet in only 800 samples, significantly outperforming AmoebaNet with 33× fewer samples. Our code is publicly available at https://github.com/facebookresearch/LaMCTS.
Transformer Based Deliberation for Two-Pass Speech Recognition

Interactive speech recognition systems must generate words quickly while also producing accurate results. Two-pass models excel at these requirements by employing a first-pass decoder that quickly emits words, and a second-pass decoder that requires more context but is more accurate. Previous work has established that a deliberation network can be an effective second-pass model. The model attends to two kinds of inputs at once: encoded audio frames and the hypothesis text from the first-pass model. In this work, we explore using transformer layers instead of long-short term memory (LSTM) layers for deliberation rescoring. In transformer layers, we generalize the "encoder-decoder" attention to attend to both encoded audio and first-pass text hypotheses. The output context vectors are then combined by a merger layer. Compared to LSTM-based deliberation, our best transformer deliberation achieves 7% relative word error rate improvements along with a 38% reduction in computation. We also compare against non-deliberation transformer rescoring, and find a 9% relative improvement.
Sentiment Analysis and Product Review Classification in E-commerce Platform

Online shopping is becoming one of the most de-manding everyday needs, nowadays. These days people are feeling comfortable shopping online. The number of its customers is increasing day by day as well as raising some problems. The major problem is that the customers can not choose the quality-full product by reading every review of an online product. Besides, the product reviews are helpful to improve the services of an e-commerce site but required huge manpower and time. We have focused on Bangla text and aimed to solve these problems by the application of Deep Neural Network (DNN) and Natural Language Processing (NLP). In this study, we have proposed two deep learning NLP models: one is for sentiment analysis and the other one is for Product Review Classification intended to improve both the quality and services. Significantly, our proposed models result in high accuracy: 0.84 and 0.69 for both Sentiment Analysis and Product Review Classification, respectively. Undoubtedly, these models can help the customers to choose the right product and the service provider to improve their services.
An improved memetic algebraic differential evolution for solving the multidimensional two-way number partitioning problem

In this article, we propose a novel and effective evolutionary algorithm for the challenging combinatorial optimization problem known as Multidimensional Two-Way Number Partitioning Problem (MDTWNPP). Since the MDTWNPP has been proven to be NP-hard, in the recent years, it has been increasingly addressed by means of meta-heuristic approaches. Nevertheless, previous proposals in literature do not make full use of critical problem information that may improve the effectiveness of the search. Here, we bridge this gap by designing an improved Memetic Algebraic Differential Evolution (iMADEB) algorithm that incorporates critical information about the problem. In particular, iMADEB evolves a population of candidate local optimal solutions by adopting three key design concepts: a novel non-redundant bit-string representation which maps population individuals one-to-one to MDTWNPP solutions, a smoother local search operator purposely designed for the MDTWNPP landscapes, and a self-adaptive algebraic differential mutation scheme built on the basis of the Lévy flight concept which automatically regulates the exploration-exploitation trade-off of the search. Computational experiments have been conducted on a widely accepted benchmark suite for the MDTWNPP with a twofold purpose: analyzing the robustness of iMADEB and compare its effectiveness with respect to the state-of-the-art approaches to date for the MDTWNPP. The experimental results provide important indications about iMADEB robustness and, most importantly, clearly show that iMADEB is the new state-of-the-art algorithm for the MDTWNPP.
Defending Your Voice: Adversarial Attack on Voice Conversion

Substantial improvements have been achieved in recent years in voice conversion, which converts the speaker characteristics of an utterance into those of another speaker without changing the linguistic content of the utterance. Nonetheless, the improved conversion technologies also led to concerns about privacy and authentication. It thus becomes highly desired to be able to prevent one's voice from being improperly utilized with such voice conversion technologies. This is why we report in this paper the first known attempt to perform adversarial attack on voice conversion. We introduce human imperceptible noise into the utterances of a speaker whose voice is to be defended. Given these adversarial examples, voice conversion models cannot convert other utterances so as to sound like being produced by the defended speaker. Preliminary experiments were conducted on two currently state-of-the-art zero-shot voice conversion models. Objective and subjective evaluation results in both white-box and black-box scenarios are reported. It was shown that the speaker characteristics of the converted utterances were made obviously different from those of the defended speaker, while the adversarial examples of the defended speaker are not distinguishable from the authentic utterances.
Security Issues in Cloud Computing

Cloud computing services like software as a service (SaaS), platform as a service (PaaS), and infrastructure as a service (IaaS) are delivery models which provides software, application platform, and infrastructure recourses to consumer respectively. This paper presents service models, deployment models, and essential characteristics of cloud computing and cloud security and privacy issues. This paper also describes detailed literature on cloud security issues and layerwise classification of each issue. The various vulnerabilities, attacks, and defense mechanisms are provided for securing cloud environment
Evaluating Multilingual Text Encoders for Unsupervised Cross-Lingual Retrieval

Pretrained multilingual text encoders based on neural Transformer architectures, such as multilingual BERT (mBERT) and XLM, have achieved strong performance on a myriad of language understanding tasks. Consequently, they have been adopted as a go-to paradigm for multilingual and cross-lingual representation learning and transfer, rendering cross-lingual word embeddings (CLWEs) effectively obsolete. However, questions remain to which extent this finding generalizes 1) to unsupervised settings and 2) for ad-hoc cross-lingual IR (CLIR) tasks. Therefore, in this work we present a systematic empirical study focused on the suitability of the state-of-the-art multilingual encoders for cross-lingual document and sentence retrieval tasks across a large number of language pairs. In contrast to supervised language understanding, our results indicate that for unsupervised document-level CLIR – a setup with no relevance judgments for IR-specific fine-tuning – pretrained encoders fail to significantly outperform models based on CLWEs. For sentence-level CLIR, we demonstrate that state-of-the-art performance can be achieved. However, the peak performance is not met using the general-purpose multilingual text encoders 'off-the-shelf', but rather relying on their variants that have been further specialized for sentence understanding tasks.
An Enhanced Energy Efficient Lightweight Cryptography Method for various IoT devices

Internet of Things (IoT) is an auspicious technology that will connect more number of devices through an internet. The huge number of communication expected to transmit high data securely is an important problem in recent days. In this paper, an Enhanced Energy Efficient Lightweight Cryptography Method which utilizes 8 bit manipulation principle (E3LCM) has been proposed. The proposed method has been verified for speech signal using MATLAB. The hardware complexity has been validated using Sparten3E XC3S500E FPGA devices and it has been found that the proposed method consumes 202mW power and 0.9 Kbytes RAM and it outperforms other methods.
Lithology Discrimination Using Sentinel-1 Dual-Pol Data and SRTM Data

Compared to various optical remote sensing data, studies on the performance of dual-pol Synthetic aperture radar (SAR) on lithology discrimination are scarce. This study aimed at using Sentinel-1 data to distinguish dolomite, andesite, limestone, sandstone, and granite rock types. The backscatter coefficients VV and VH, the ratio VV–VH; the decomposition parameters Entropy, Anisotropy, and Alpha were firstly derived and the Kruskal–Wallis rank sum test was then applied to these polarimetric derived matrices to assess the significance of statistical differences among different rocks. Further, the corresponding gray-level co-occurrence matrices (GLCM) features were calculated. To reduce the redundancy and data dimension, the principal component analysis (PCA) was carried out on the GLCM features. Due to the limited rock samples, before the lithology discrimination, the input variables were selected. Several classifiers were then used for lithology discrimination. The discrimination models were evaluated by overall accuracy, confusion matrices, and the area under the curve-receiver operating characteristics (AUC-ROC). Results show that (1) the statistical differences of the polarimetric derived matrices (backscatter coefficients, ratio, and decomposition parameters) among different rocks was insignificant; (2) texture information derived from Sentinel-1 had great potential for lithology discrimination; (3) partial least square discrimination analysis (PLSDA) had the highest overall accuracy (0.444) among the classification models; (4) though the overall accuracy is unsatisfactory, according to the AUC-ROC and confusion matrices, the predictive ability of PLSDA model for limestone is high with an AUC value of 0.8017, followed by dolomite with an AUC value of 0.7204. From the results, we suggest that the dual-pol Sentinel-1 data are able to correctly distinguish specific rocks and has the potential to capture the variation of different rocks.
A noisy label and negative sample robust loss function for DNN-based distant supervised relation extraction

As a major method for relation extraction, distantly supervised relation extraction (DSRE) suffered from the noisy label problem and class imbalance problem (these two problems are also common for many other NLP tasks, e.g., text classification). However, there seems no existing research in DSRE or other NLP tasks that can simultaneously solve both problems, which is a significant insufficiency in related researches. In this paper, we propose a loss function which is robust to noisy label and efficient for the imbalanced class dataset. More specific, first we quantify the negative impacts of the noisy label and class imbalance problems. And then we construct a loss function that can minimize these negative impacts through a linear programming method. As far as we know, this seems to be the first attempt to address the noisy label problem and class imbalance problem simultaneously. We evaluated the constructed loss function on the distantly labeled dataset, our artificially noised dataset, human-annotated dataset of Docred, as well as the artificially noised dataset of CoNLL 2003. Experimental results indicate that a DNN model adopting the constructed loss function can outperform other models that adopt the state-of-the-art noisy label robust or negative sample robust loss functions.
De-rendering the World’s Revolutionary Artefacts

Recent works have shown exciting results in unsupervised image de-rendering—learning to decompose 3D shape, appearance, and lighting from single-image collections without explicit supervision. However, many of these assume simplistic material and lighting models. We propose a method, termed RADAR, that can recover environment illumination and surface materials from real single-image collections, relying neither on explicit 3D supervision, nor on multi-view or multi-light images. Specifically, we focus on rotationally symmetric artefacts that exhibit challenging surface properties including specular reflections, such as vases. We introduce a novel self-supervised albedo discriminator, which allows the model to recover plausible albedo without requiring any ground-truth during training. In conjunction with a shape reconstruction module exploiting rotational symmetry, we present an end-to-end learning framework that is able to de-render the world’s revolutionary artefacts. We conduct experiments on a real vase dataset and demonstrate compelling decomposition results, allowing for applications including free-viewpoint rendering and relighting. More results and code at: https://sorderender.github.io/.
Dataset of Pakistan Sign Language and Automatic Recognition of Hand Configuration of Urdu Alphabet through Machine Learning

Social correspondence is one of the most significant columns that the public dependent on. Notably, language is the best way to communicate and associate with one another both verbally and nonverbally. There is a persistent communication gap among deaf and non-deaf communities because non-deaf people have less understanding of sign languages. Every region/country has its sign language. In Pakistan, the sign language of Urdu is a visual gesture language that is being used for communication among deaf peoples. However, the dataset of Pakistan Sign Language (PSL) is not available publicly. The dataset of PSL has been generated by acquiring images of different hand configurations through a webcam. In this work, 40 images of each hand configuration with multiple orientations have been captured. In addition, we developed, an interactive android mobile application based on machine learning that minimized the communication barrier between the deaf and non-deaf communities by using the PSL dataset. The android application recognizes the Urdu alphabet from input hand configuration.
Toward confidential cloud computing

Extending hardware-enforced cryptographic protection to data while in use.
An Introductory Survey on Attention Mechanisms in Computer Vision Problems

Attention mechanism is a simple method derived from the research of human vision. With the development of artificial intelligence, it is gradually applied to the field of deep learning, especially computer vision. The weight of the pixels is redistributed in the form of a mask to compose attention mechanism. Various tasks in the field of computer vision, including image classification, object detection, image semantic segmentation, and fine-grained classification, have used attention mechanism widely and significantly improved. This paper presents an introductory summary of the mechanisms in computer vision problems through recent research. Provide readers with the basics of this widely used method, explore its manifestations under different tasks, evaluate the performance of attention mechanisms, and look forward to its development trends.
Exploring the Main Determinants of Mobile Learning Application Usage During Covid-19 Pandemic in Jordanian Universities

Recently, higher education sector has been affected by Covid-19 pandemic significantly. Where, several universities have started to adopt online distance learning tools such as mobile learning applications. However, in order to success mobile learning applications during this pandemic, it is important to understand the necessary factors that ensure the actual use among students in post implementation. The findings showed that factors of technology, awareness, training and experience had a significant and positive influence on the actual use of mobile learning applications. While, the results indicated that psychological factors had a negative effect on the actual use. Furthermore, the results also revealed that technological and individual factors play a crucial role in solving the psychological issues among students. The findings of this research will offer useful recommendations for educational institutions in order to encourage the use of mobile learning applications effectively during Covid-19 pandemic.
Considering breast density for the classification of benign and malignant mammograms

Mammography plays a crucial role in breast cancer screening because it can be used to diagnose a breast mass and breast calcification region early. Mammogram has some drawbacks; for example, detecting breast cancer masses in extremely dense breast tissue is considerably difficult. Thus, this study combined breast density with benign and malignant masses to classify breast tumors. This study used data augmentation to preprocess mammograms and then convolutional neural network (CNN) models namely AlexNet, DenseNet, and ShuffleNet to classify the images. The accuracies of the three models before and after data augmentation were compared. Before data augmentation, the accuracies for training and testing sets of AlexNet were 40.47 % and 38.57 %, respectively, those of DenseNet were 90.00 % and 46.39 %, respectively, and those of ShuffleNet were 96.48 % and 42.86 %, respectively. After data augmentation, the accuracies for training and testing sets of AlexNet were 99.35 % and 95.46 %, respectively, those of DenseNet were 99.91 % and 99.72 %, respectively, and those of ShuffleNet were 99.85 % and 97.84 % respectively. Data augmentation allows the model to learn more pictures of different situations and angles to accurately classify new images. Although the accuracy of DenseNet was the highest, the execution time required was considerably longer than that required for ShuffleNet. Considering the accuracy and execution time, ShuffleNet is recommended to be used for the classification of benign and malignant mammography breast tumors.
Rethinking Image Salient Object Detection: Object-Level Semantic Saliency Reranking First, Pixelwise Saliency Refinement Later

Human attention is an interactive activity between our visual system and our brain, using both low-level visual stimulus and high-level semantic information. Previous image salient object detection (SOD) studies conduct their saliency predictions via a multitask methodology in which pixelwise saliency regression and segmentation-like saliency refinement are conducted simultaneously. However, this multitask methodology has one critical limitation: the semantic information embedded in feature backbones might be degenerated during the training process. Our visual attention is determined mainly by semantic information, which is evidenced by our tendency to pay more attention to semantically salient regions even if these regions are not the most perceptually salient at first glance. This fact clearly contradicts the widely used multitask methodology mentioned above. To address this issue, this paper divides the SOD problem into two sequential steps. First, we devise a lightweight, weakly supervised deep network to coarsely locate the semantically salient regions. Next, as a postprocessing refinement, we selectively fuse multiple off-the-shelf deep models on the semantically salient regions identified by the previous step to formulate a pixelwise saliency map. Compared with the state-of-the-art (SOTA) models that focus on learning the pixelwise saliency in single images using only perceptual clues, our method aims at investigating the object-level semantic ranks between multiple images, of which the methodology is more consistent with the human attention mechanism. Our method is simple yet effective, and it is the first attempt to consider salient object detection as mainly an object-level semantic reranking problem.
GCPS: A CNN Performance Evaluation Criterion for Radar Signal Intrapulse Modulation Recognition

In radar intrapulse modulation recognition through convolutional neural networks (CNN), the recognition rate vis-a-vis varying signal-to-noise ratios (SNRs) is utilized to analyze the performance of networks. However, three shortcomings exist in this realm, (1) Proportional increase in recognition rate up to 100%, due to SNR increase, leading to saturation. (2) The dependency of recognition rate on the test set. (3) Recognition rate failing to substantiate the increase in SNR and its manifestation in CNN performance enhancement. Foregone in view, this letter proposes an evaluation criterion to evaluate CNN performance to address the shortcomings above. We propose Grad-CAM position score-Internal Benchmark (GCPS-IB) to address (1), (2), and Grad-CAM position score-External Benchmark (GCPS-EB) to address (3). Finally, we utilize GCPS to evaluate and compare the performance of existing GoogLeNet and ResNet-18 to verify the efficacy. The proposed criterion establishes its efficacy by addressing the shortcomings mentioned above, hence augmenting the recognition rate.