Reviewerly/exHarmony huggingface dataset:
Digital Forensics and Incident Response (DFIR) Challenges in IoT Platforms

The rapid progress experienced in the Internet of Things (IoT) space is one that has introduced new and unique challenges for cybersecurity and IoT-Forensics. One of these problems is how digital forensics and incident response (DFIR) are handled in IoT. Since enormous users use IoT platforms to accomplish their day to day task, massive amounts of data streams are transferred with limited hardware resources; conducting DFIR needs a new approach to mitigate digital evidence and incident response challenges owing to the facts that there are no unified standard or classified principles for IoT forensics. Today's IoT DFIR relies on self-defined best practices and experiences. Given these challenges, IoT-related incidents need a more structured approach in identifying problems of DFIR. In this paper, we examined the major DFIR challenges in IoT by exploring the different phases involved in a DFIR when responding to IoT-related incidents. This study aims to provide researchers and practitioners a road-map that will help improve the standards of IoT security and DFIR.
Automatic Image Annotation using Tag Relations and Graph Convolutional Networks

Automatic image annotation is a mechanism to assign a list of appropriate tags that describe the visual content of a given image. Most methods only focus on the content of the images and ignore the relationship between the tags in vocabulary. In this work, we propose a new deep learning-based automatic image annotation architecture, which considers label dependencies in a graph convolution neural network structure and extracts tag descriptors to re-weight the output class scores based on their relationships. The proposed architecture has three main parts: feature extraction, graph convolutional network, and annotation. In graph convolutional network, we apply one layer convolution on vocabulary graph to get some tag descriptors that are applied on the image features. In the annotation part, we use two previous algorithms to rectify the tags. Compared to related work, the experimental results using Corel5k, Esp Game, and IAPRTC-12 datasets indicate the best performance of the annotating model with F1_score in IAPRTC-12 and the second-best result with F1_score and N <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">+</sup> measure in Corel5K and finally the second-best result with F1_score in ESP Game.
BERT for Sentiment Classification in Software Engineering

Sentiment analysis (SA) has been applied to various fields of software engineering (SE), such as app reviews, stack overflow Q&A website and API comments. General SA tools are trained based on movie or product review data. Research has shown that these SA tools can produce negative results when applied to the field of SE. In order to overcome the above limitations, developers need to customize tools (e.g., SentiStrength-SE, SentiCR, Senti4SD). In recent years, the pre-trained transformer-based models have brought great breakthroughs in the field of natural language processing. Therefore, we intend to fine-tune the pre-trained model BERT for downstream text classification tasks. We compare the performance of SE-specific tools. Meanwhile, we also studied the performance of SE-specific tools in a cross-platform setting. Experimental results show that our approach (BERT-FT) outperforms the existing state-of-the-art models in terms of F1-scores.
Learning Representation From Concurrence-Words Graph For Aspect Sentiment Classification

Abstract Aspect sentiment classification is an important research topic in natural language processing and computational linguistics, assisting in automatically review analysis and emotional tendency judgement. Different from extant methods that focus on text sequence representations, this paper presents a network framework to learn representation from concurrence-words relation graph (LRCWG), so as to improve the Macro-F1 and accuracy. The LRCWG first employs the multi-head attention mechanism to capture the sentiment representation from the sentences which can learn the importance of text sequence representation. And then, it leverages the priori sentiment dictionary information to construct the concurrence relations of sentiment words with Graph Convolution Network (GCN). This assists in that the learnt context representation can keep both the semantics integrity and the features of sentiment concurrence-words relations. The designed algorithm is experimentally evaluated with all the five benchmark datasets and demonstrated that the proposed aspect sentiment classification can significantly improve the prediction performance of learning task.
3D Fiber Segmentation with Deep Center Regression and Geometric Clustering

Material and biological sciences frequently generate large amounts of microscope data that require 3D object-level segmentation. Often, the objects of interest have a common geometry, for example spherical, ellipsoidal, or cylindrical shapes. Neural networks have became a popular approach for object detection but they are often limited by their training dataset and have difficulties adapting to new data. In this paper, we propose a volumetric object detection approach for microscopy volumes comprised of fibrous structures by using deep centroid regression and geometric regularization. To this end, we train encoder-decoder networks for segmentation and centroid regression. We use the regression information combined with prior system knowledge to propose cylindrical objects and enforce geometric regularization in the segmentation. We train our networks on synthetic data and then test the trained networks in several experimental datasets. Our approach shows competitive results against other 3D segmentation methods when tested on the synthetic data and outperforms those other methods across different datasets. The reference codes and pytorch dictionaries are available at https://github.com/camilo-aguilar/3D-Fiber-Segmentation.
Integration of Global and Local Features for Specular Reflection Inpainting in Colposcopic Images

To explore an inpainting method that can balance texture details and visual observability to eliminate the specular reflection (SR) regions in the colposcopic image, thus improving the accuracy of clinical diagnosis for cervical cancer.(1) To ensure smoothness, Gaussian Blur and filling methods are applied to the global image. (2) Striving to preserve the anatomical texture details of the colposcopic image as much as possible, the exemplar-based method is applied to local blocks. (3) The colposcopic images inpainted in the previous two steps are integrated, so that important information of non-SR regions is preserved based on eliminating SR regions.In the subjective visual assessment of inpainting results, the average of 3.55 ranks first in the five comparison sets. As to the clinical test, comparing the diagnosis results of 6 physicians before and after eliminating SR regions, the average accuracy of two kinds of classifications increased by 1.44% and 2.03%, respectively.This method can effectively eliminate the SR regions in the colposcopy image and present a satisfactory visual effect. Significance. As a preprocessing method for computer-aided diagnosis systems, it can also improve physicians' accuracy in clinical diagnosis.
Pre-deployment Security Assessment for Cloud Services Through Semantic Reasoning

Abstract Over the past ten years, the adoption of cloud services has grown rapidly, leading to the introduction of automated deployment tools to address the scale and complexity of the infrastructure companies and users deploy. Without the aid of automation, ensuring the security of an ever-increasing number of deployments becomes more and more challenging. To the best of our knowledge, no formal automated technique currently exists to verify cloud deployments during the design phase. In this case study, we show that Description Logic modeling and inference capabilities can be used to improve the safety of cloud configurations. We focus on the Amazon Web Services (AWS) proprietary declarative language, CloudFormation, and develop a tool to encode template files into logic. We query the resulting models with properties related to security posture and report on our findings. By extending the models with dataflow-specific knowledge, we use more comprehensive semantic reasoning to further support security reviews. When applying the developed toolchain to publicly available deployment files, we find numerous violations of widely-recognized security best practices, which suggests that streamlining the methodologies developed for this case study would be beneficial.
SI-BBA – A novel phishing website detection based on Swarm intelligence with deep learning

Websites phishing is one of several defense coercions to Internet Service Provider. Mainly web phishing focused on stealing private information such as username, password, and credit card details too through imitating a legal creature. Deep learning based Neural Networks are extensively used for phishing detection with high accuracy measures and metrics. In this proposed work, an improved version of Binary Bat namely Swarm Intelligence Binary Bat Algorithm is used for designing the neural network which categorize the network URL websites similar to classification approach. It is utilized for the initial moment in this domain of relevance to the preeminent of our understanding. Our experimental results shows that deep learning based Adam optimizer reaches high classification accuracy as 94.8% in phishing websites attack detection based on swarm intelligence technique.
A Diverse Noise-Resilient DNN Ensemble Model on Edge Devices for Time-Series Data

Many applications such as healthcare and transportation on edge devices will use deep neural network (DNN) prediction based on time-series data collected by the devices. However, the existence of noises in the on-device sensors negatively impacts the sensing output of the DNN models. The state-of-the-art time-series based DNN approaches can deal with Gaussian noise but cannot effectively handle other types of noises in spite of the existence of different types of noises such as shot, burst, transient noises, and their combination. In this paper, we propose an ensemble-based DNN model, namely E-Sense, which consists of different expert models for different noises and shows higher prediction accuracy. Since an edge device may have limited resources to run a large DNN model, we further propose a novel searching-based model compression method called E-Comp that uses knowledge distillation to compress E-Sense to a smaller DNN model while maintaining the accuracy. Our real experiments on live sensor data and trace-driven experiments on three real traces show that E-Sense outperforms other methods in accuracy, and E-Comp reduces 27% inference time without sacrificing accuracy compared with other DNN compression methods. We also distributed our source code.
Dynamic Obstacle Avoidance of Mobile Robot Based on Adaptive Velocity Obstacle

The Velocity Obstacle (VO) provides a good solution for mobile robot collision avoidance problem. VO divides the velocity region into collision zone and non-collision zone. The robot can avoid collisions by selecting the velocity in the non-collision zone. However, if the robot chooses the critical velocity of the collision cone to minimize the time consumption, the error of the sensor will lead to the collision. To solve this problem, the Adaptive Velocity Obstacle (AVO) is proposed which has a good balance between safety and efficiency. The expansion radius of the moving obstacle is adjusted adaptively according to the relative velocity and distance between the robot and the obstacle. As the radius changes, the collision cone can also change dynamically so that the robot can maintain a safe path under the sensor detection error while ensuring real-time performance. After, the Dynamic Window Approach (DWA) is used to get the local path through optimizing the velocity outside the collision cone. Finally, we carried out comparative simulations. Results show that the AVO can reduce the chance of collision under sensor error.
Did the Arab Spring lead to economic, institutional and sociopolitical changes? An empirical analysis through the perceptions of Egyptian youth

Purpose Egyptian youth played a central role in the Arab Spring (AS) uprisings, demanding bread, justice and opportunities. This paper examines the perceptions of young Egyptians about the AS through their responses on the economic, institutional and sociopolitical conditions in the pre-AS and post-AS periods. Design/methodology/approach The empirical analysis relies on a micro-level dataset extracted from the SAHWA Youth Survey. The estimations are carried out through the bivariate ordered probit model. Findings The results reveal that the perceptions about the AS-related outcomes are not uniform, and that social values and ideological characteristics matter more than the standard socioeconomic attributes in comprehending the responses. They indicate that individuals with secularist, non-traditionalist and gender equality inclinations have generally formed more favorable perceptions about the AS-related changes. Also, the results suggest that the AS has generated propitious perceived conditions for further global connection, relative to the Arab and Islamic ties. They show that the perceptions of individuals who place credence in entrepreneurial attitude have been relatively unfavorable vis-à-vis the post-AS conditions. Originality/value The findings underscore the importance of comprehending the perceptions about the AS-related outcomes. They imply that the AS has produced “winners” and “losers,” and has laid down the basis for social transformations in Egypt.
Problems of Using Redactable Blockchain Technology

Blockchain Technology (BT) involves maintaining a special log (or register of records), the amount of information in which increases with each new record. Maintaining such a registry in high-load systems, in which several thousand (or more) records can be added per second, creates a serious problem of storing such a volume of information. There are also cases, in which the blockchain security features, and in particular its immutable data storage, are a drawback. For instance, once stored in the blockchain, erroneous data cannot be removed. To solve this problem, it is possible to use the redactable BT (RBT). In this case, one can clear the registry of outdated (or unnecessary) information that is no longer suitable for solving the tasks assigned to the system. However, this issue has its own subtleties, features and rules for working with this technology. Therefore, the purpose of the paper is to identify the actual problems of the RBT usage.
A Suitability Analysis of Software Based Testing Strategies for the On-line Testing of Artificial Neural Networks Applications in Embedded Devices

Electronic devices based on artificial intelligence solutions are pervading our everyday life. Nowadays, human decision processes are supported by real-time data gathered from intelligent systems. Artificial Neural Networks (ANNs) are one of the most used deep learning predictive models due to their outstanding computational capabilities. However, assessing their reliability is still an open issue faced by both the academic and industrial worlds, especially when ANNs are deployed on safety-critical systems, such as self-driving cars in the automotive world. In these systems, a strategy for identifying hardware faults is required by industry standards (e.g., ISO26262 for automotive, and DO254 for avionics). Among the existing in-field test strategies, the periodic scheduling of on-line Software Test Library (STL) is a wide strategy adopted; STL allows to reach an acceptable fault coverage without the need for additional hardware. However, when dealing with ANN-based applications, the execution of on-line tests interleaving the ANN inferences may jeopardise the strive for performance maximization. The paper presents a comprehensive analysis of six possible scenarios concerning the execution of on-line self-test programs in embedded devices running ANN-based applications. In the proposed scenarios, the impact of the STL execution on the ANN performance is analyzed; in particular, the execution times of an inference and the Fault Detection Time (FDT) of the STL are discussed and compared. Experimental analyses are provided by relying on: an open-source RISC-V platform running two different convolutional neural networks; a STL for RISC-V cores with a maximum achievable fault coverage of 90%.
HeSA: Heterogeneous Systolic Array Architecture for Compact CNNs Hardware Accelerators

Compact convolutional neural networks have become a hot research topic. However, we find that the hardware accelerator with systolic arrays processing compact models is extremely performance-inefficient, especially when processing depthwise convolutional layers in the networks. To make systolic arrays efficient for compact convolutional neural networks, we propose the heterogeneous systolic array (HeSA) architecture. It introduces heterogeneous processing elements that support multiple modes of dataflow, which can further exploit the reuse data chance of depthwise convolutional layers and without changing the architecture of the naïve systolic array. By increasing the utilization rate of processing elements in the array, HeSA improves the performance, throughput, and energy efficiency compared to the standard baseline. Based on our evaluation with typical workloads, HeSA improves the utilization rate of the computing resource in depthwise convolutional layers by 4.5×-5.5× and acquires 1.5-2.2× total performance speedup compared to the standard systolic array architecture. HeSA also improves the on-chip data reuse chance and saves over 20% of energy consumption. Meanwhile, the area of HeSA is basically unchanged compared to the baseline due to its simple design.
AI-Powered MOOCs: Video Lecture Generation

This paper focuses on the application of artificial intelligence in education that artificial intelligence technology was utilized to reduce the instructor's workload as well as enhance learner engagement in online learning platforms. The idea of this paper is to use text-to-speech and speech-driven-face to automatically create a video lecture with the instructor's voice and face without recording the video. This approach not only gives the best display of lesson content of video lectures which contains slides (pdf format), instructor's voice, and face but also enables the modification of video lectures without recapture video. Furthermore, this approach allows learners to choose a video lecture with their desired instructor's voice and face. The experimental results validated our approach.
Who's Responsible? Jointly Quantifying the Contribution of the Learning Algorithm and Data

A learning algorithm A trained on a dataset D is revealed to have poor performance on some subpopulation at test time. Where should the responsibility for this lay? It can be argued that the data is responsible, if for example training A on a more representative dataset D' would have improved the performance. But it can similarly be argued that A itself is at fault, if training a different variant A' on the same dataset D would have improved performance. As ML becomes widespread and such failure cases more common, these types of questions are proving to be far from hypothetical. With this motivation in mind, in this work we provide a rigorous formulation of the joint credit assignment problem between a learning algorithm A and a dataset D. We propose Extended Shapley as a principled framework for this problem, and experiment empirically with how it can be used to address questions of ML accountability.
“You, Move There!”: Investigating the Impact of Feedback on Voice Control in Virtual Environments

Current virtual environment (VEs) input techniques often overlook speech as a useful control modality. Speech could improve interaction in multimodal VEs by enabling users to address objects, locations, and agents, yet research on how to design effective speech for VEs is limited. Our paper investigates the effect of agent feedback on speech VE experiences. Through a lab study, users commanded agents to navigate a VE, receiving either auditory, visual or behavioural feedback. Based on a post interaction semi-structured interview, we find that the type of feedback given by agents is critical to user experience. Specifically auditory mechanisms are preferred, allowing users to engage with other modalities seamlessly during interaction. Although command-like utterances were frequently used, it was perceived as contextually appropriate, ensuring users were understood. Many also found it difficult to discover speech-based functionality. Drawing on these, we discuss key challenges for designing speech input for VEs.
Data Sampling-Based Feature Selection Framework for Software Defect Prediction

High dimensionality and class imbalance are latent data quality problems that have a negative effect on the predictive capabilities of prediction models in software defect prediction (SDP). As a viable solution, data sampling and feature selection (FS) has been used to address the class imbalance and high dimensionality problem respectively. Most of the existing studies in SDP addressed these data quality problem individually which often affects the generalizability of such studies. Hence, this study proposed a novel framework based on correlation-based feature selection (CFS) and synthetic minority oversampling (SMOTE) methods for software defect prediction. CFS based on best-first search (BFS) method is used to handle feature selection while SMOTE sampling technique is used for the class imbalance. The proposed framework was developed with Bayesian Networks (BN) and Decision Tree (DT) classifiers on defects dataset from NASA repository. The experimental results showed that the proposed framework outperformed other experimental methods. The high accuracy (83.84%) and AUC (0.90) values of the proposed framework indicates its ability to differentiate between the defective and non-defective labels without bias. The proposed framework is therefore recommended for SDP as it handles both class imbalance and high dimensionality in SDP with high prediction performance.
Smart Governance and COVID-19 Control in Wuhan, China

In dealing with the global COVID-19 pandemic, China has achieved reasonable success in governing COVID-19 within two months with the help of technologies. This study specifically focuses on how these massive technologies have been implemented to facilitate the smart governance of COVID-19 in Wuhan, China. By discursively analyzing existing data from multiple sources, the results obtained in this chapter show that the real 'smartness' of the smart governance of COVID-19 in Wuhan is the innovative use of technologies to develop different types of governance approaches to control COVID-19 in an effective and targeted way. As the pandemic continues to evolve worldwide, lessons learned from Wuhan, China can be beneficial to other countries in different institutional contexts to build their own, context-specific governance for controlling the pandemic.