Reviewerly/exHarmony huggingface dataset:
Toward Secure and Efficient Deep Learning Inference in Dependable IoT Systems

The rapid development of deep learning (DL) enables resource-constrained systems and devices [e.g., Internet of Things (IoT)] to perform sophisticated artificial intelligence (AI) applications. However, AI models, such as deep neural networks (DNNs), are known to be vulnerable to adversarial examples (AEs). Past works on defending against AEs require heavy computations in the model training or inference processes, making them impractical to be applied in IoT systems. In this article, we propose a novel method, Super-IoT, to enhance the security and efficiency of AI applications in distributed IoT systems. Specifically, Super-IoT utilizes a pixel drop operation to eliminate adversarial perturbations from the input and reduce network transmission throughput. Then, it adopts a sparse signal recovery method to reconstruct the dropped pixels and wavelet-based denoising method to reduce the artificial noise. Super-IoT is a lightweight method with negligible computation cost to IoT devices and little impact on the DNN model performance. Extensive evaluations show that it can outperform three existing AE defensive solutions against most of the AE attacks with better transmission efficiency.
End-to-end deep learning-based fringe projection framework for 3D profiling of objects

Fringe projection techniques are widely used for precise three-dimensional depth profiling of objects. Existing signal processing-based fringe projection techniques measure the phase deformation of the projected fringe patterns with a sequence of operations, such as fringe denoising, fringe analysis for wrapped phase extraction, followed by phase unwrapping. However, the error induced in any of the stages leads to erroneous depth estimation. Furthermore, any aliasing in frequency domain fringe analysis and ill-posed nature of phase unwrapping limit the overall accuracy of the Fringe Projection Profilometry (FPP). To this end, unlike the traditional approaches, we propose a paradigm shift by introducing a novel end-to-end deep learning-based framework for FPP that does not need any frequency domain filtering and phase unwrapping. The proposed framework directly reconstructs the object's depth profile from the deformed fringe itself through a multi-resolution similarity assessment convolutional neural network. We compare the performance of the proposed framework with two widely used conventional approaches. The evaluations are performed for various challenging and important scenarios such as low fringe-frequency, depth profiles with high dynamic range and noisy fringes. The results demonstrate that the proposed framework achieves promising results in all these scenarios.
Lattice-Based Blind Signatures, Revisited

We observe that all previously known lattice-based blind signature schemes contain subtle flaws in their security proofs (e.g., Rückert, ASIACRYPT '08) or can be attacked (e.g., BLAZE by Alkadri et al., FC '20). Motivated by this, we revisit the problem of constructing blind signatures from standard lattice assumptions. We propose a new three-round lattice-based blind signature scheme whose security can be proved, in the random oracle model, from the standard SIS assumption. Our starting point is a modified version of the (insecure) BLAZE scheme, which itself is based Lyubashevsky's three-round identification scheme combined with a new aborting technique to reduce the correctness error. Our proof builds upon and extends the recent modular framework for blind signatures of Hauck, Kiltz, and Loss (EUROCRYPT '19). It also introduces several new techniques to overcome the additional challenges posed by the correctness error which is inherent to all lattice-based constructions. While our construction is mostly of theoretical interest, we believe it to be an important stepping stone for future works in this area.
Measuring and Characterizing Hate Speech on News Websites

The Web has become the main source for news acquisition. At the same time, news discussion has become more social: users can post comments on news articles or discuss news articles on other platforms like Reddit. These features empower and enable discussions among the users; however, they also act as the medium for the dissemination of toxic discourse and hate speech. The research community lacks a general understanding on what type of content attracts hateful discourse and the possible effects of social networks on the commenting activity on news articles.
SLPoW: Secure and Low Latency Proof of Work Protocol for Blockchain in Green IoT Networks

Traditional Internet of Things (IoT) system architectures are centralized. Data from the devices are stored on the back-end, where they are processed and analyzed, and then reconnected to IoT devices. The scalability of centralized systems is very limited especially when an abundance of devices exist on an IoT network. Network security in IoT networks is another aspect at stake that could be compromised easily due to the unavailability of security in design mechanisms in most IoT networks. Blockchain technology is a distributed ledger without any intensive management that can store all transactions which leads to large amounts of data that increases over time. Large data amounts will be more pronounced with the increasing IoT devices and blockchain use cases involving IoT. IoT devices are for the most part constrained in both energy, storage, and computation, unlikely to be able to store all blockchain data. The current implementation of blockchain is not IoT friendly. Moreover, consensus on the blockchain using Proof of Work (PoW) is infeasible due to computational constraints. In this paper, we propose a Secure and Low latency Proof of Work (SLPoW) protocol. We also bring the computation of miners onto a Field-programmable gate array (FPGA) to improve the processing speeds of computation. We consider our resulting blockchain technology using SLPoW suitable for the evolving Green IoT setting.
Which politicians receive abuse? Four factors illuminated in the UK general election 2019

The 2019 UK general election took place against a background of rising online hostility levels toward politicians, and concerns about the impact of this on democracy, as a record number of politicians cited the abuse they had been receiving as a reason for not standing for re-election. We present a four-factor framework in understanding who receives online abuse and why. The four factors are prominence, events, online engagement and personal characteristics. We collected 4.2 million tweets sent to or from election candidates in the six week period spanning from the start of November until shortly after the December 12th election. We found abuse in 4.46% of replies received by candidates, up from 3.27% in the matching period for the 2017 UK general election. Abuse levels have also been climbing month on month throughout 2019. Abuse also escalated throughout the campaign period. Abuse focused mainly on a small number of high profile politicians, with the most prominent individuals receiving not only more abuse by volume, but also as a percentage of replies. Abuse is “spiky”, triggered by external events such as debates, or certain tweets. Some tweets may become viral targets for personal abuse. On average, men received more general and political abuse; women received more sexist abuse. Conservative candidates received more political and general abuse. We find that individuals choosing not to stand for re-election had received more abuse across the preceding year.
KiRTi: A Blockchain-Based Credit Recommender System for Financial Institutions

In this paper, we propose KiRTi, a deep-learning-based credit-recommender scheme for public blockchain to facilitate smart lending operations between prospective borrowers (PB) and prospective lenders (PL) to eliminate the need of third party credit-rating agencies (CRAs) for credit-score (CS) generation. Thus loan grants to PB from PL is secured, authorized, and automated so as to expedite the disbursement process. KiRTi stores PB historical transactions, current assets, and liabilities as time-series sequenced data in a public blockchain. The sequenced data is fetched from blockchain by a long-short term memory (LSTM) model that generates CS for loan recommendations based on proposed lending algorithms for PB and PL. To ensure real-time updation of CS, edge-weights are updated based on boolean indicators from PB and PL, which indicates the successful repayments and loan-defaults. The process is iterated to improve the accuracy of edge-weights and generated CS to ensures the correct credibility of PB for future lending. Smart contracts (SC) are proposed for automatic setup of loan repayments between PB and PL. To model the LSTM recommender scheme, a German credit dataset from UCI repository is considered with 1000 credit-histories of PB, with 700 successful repayments and 300 defaults. KiRTi achieves an accuracy of 97.5% in comparison to conventional approaches with an F-measure of 0.98304. The security evaluation of KiRTi shows that it has computation cost of 20.96 ms and communication cost of 121 bytes compared to other state-of-the-art approaches.
A generalized weighted distance k-Nearest Neighbor for multi-label problems

In multi-label classification, each instance is associated with a set of pre-specified labels. One common approach is to use Binary Relevance (BR) paradigm to learn each label by a base classifier separately. Use of k-Nearest Neighbor (kNN) as the base classifier (denoted as BRkNN) is a simple, descriptive and powerful approach. In binary relevance a highly imbalanced view of dataset is used. However, kNN is known to perform poorly on imbalanced data. One approach to deal with this is to define the distance function in a parametric form and use the training data to adjust the parameters (i.e. adjusting boundaries between classes) by optimizing a performance measure customized for imbalanced data e.g. F-measure. Prototype Weighting (PW) scheme presented in the literature (Paredes & Vidal, 2006) uses gradient descent to specify the parameters by minimizing the classification error-rate on training data. This paper presents a generalized version of PW. First, instead of minimizing the error-rate proposed in PW, the generalized PW supports also other objective functions that use elements of confusion matrix (including F-measure). Second, PW originally presented for 1NN is extended to the general case of kNN (i.e., k>=1). For problems having highly overlapped classes, it is expected to perform better since a value of k>1 produces smoother decision boundaries which in turn can improve generalization. In multi-label problems with many labels or problems with highly overlapped classes, the proposed generalized PW is expected to significantly improve the performance as it involves many decision boundaries. The performance of the proposed method has been compared with state-of-the-art methods in multi-label classification containing 6 lazy classifiers based on kNN. Experiments show that the proposed method significantly outperforms other methods.
A qualitative exploration of teachers’ perspective on smartphones usage in higher education in developing countries

Abstract Recently, Smartphone sales have surpassed the sales of all other computing devices including desktops, laptops and tablet PCs. Moreover, Smartphones have become a primary device to access the Internet as well as the most commonly used infotainment gadget among the Generations Y and Z (those born in 80s and 90s). Despite this widespread adoption of Smartphones among the youth, its usage in higher education is still a novice idea specifically in developing countries. This study is aimed at capturing the views of faculty members regarding usage of Smartphones for promoting learning at the tertiary level. In-depth interviews of 22 faculty members from different academic departments were conducted for this purpose. Responses of the interviewees were recorded which were later transcribed and analyzed to draw inferences. Results of this study indicate that the faculty members considered Smartphones to be an effective medium for off-campus learning and communication with peers and students. They also considered Smartphones suitable for explaining complex topics to their students as audio-visual content available over the Internet can easily be shared. However, majority of the respondents were skeptical about using Smartphones for educational purposes as they considered them to be a source of distraction, wastage of time, technostress, and emotional detachment. Moreover, they reported small size of both the screen and keyboard as an important limiting factor undermining the effective use of Smartphones in education. Lack of training and support, lack of technical knowledge and background, excessive work load and lack of incentives for using technology in education were reported as major barriers in adoption of Smartphones in higher learning landscape.
PCSGAN: Perceptual cyclic-synthesized generative adversarial networks for thermal and NIR to visible image transformation

In many real world scenarios, it is difficult to capture the images in the visible light spectrum (VIS) due to bad lighting conditions. However, the images can be captured in such scenarios using Near-Infrared (NIR) and Thermal (THM) cameras. The NIR and THM images contain the limited details. Thus, there is a need to transform the images from THM/NIR to VIS for better understanding. However, it is non-trivial task due to the large domain discrepancies and lack of abundant datasets. Nowadays, Generative Adversarial Network (GAN) is able to transform the images from one domain to another domain. Most of the available GAN based methods use the combination of the adversarial and the pixel-wise losses (like $L_1$ or $L_2$) as the objective function for training. The quality of transformed images in case of THM/NIR to VIS transformation is still not up to the mark using such objective function. Thus, better objective functions are needed to improve the quality, fine details and realism of the transformed images. A new model for THM/NIR to VIS image transformation called Perceptual Cyclic-Synthesized Generative Adversarial Network (PCSGAN) is introduced to address these issues. The PCSGAN uses the combination of the perceptual (i.e., feature based) losses along with the pixel-wise and the adversarial losses. Both the quantitative and qualitative measures are used to judge the performance of the PCSGAN model over the WHU-IIP face and the RGB-NIR scene datasets. The proposed PCSGAN outperforms the state-of-the-art image transformation models, including Pix2pix, DualGAN, CycleGAN, PS2GAN, and PAN in terms of the SSIM, MSE, PSNR and LPIPS evaluation measures. The code is available at https://github.com/KishanKancharagunta/PCSGAN.
Graph-based parallel large scale structure from motion

While Structure from Motion achieves great success in 3D reconstruction, it still meets challenges on large scale scenes. Incremental SfM approaches are robust to outliers, but are limited by low efficiency and easy suffer from drift problem. Though Global SfM methods are more efficient than incremental approaches, they are sensitive to outliers, and would also meet memory limitation and time bottleneck. In this work, large scale SfM is deemed as a graph problem, where graph are respectively constructed in image clustering step and local reconstructions merging step. By leveraging the graph structure, we are able to handle large scale dataset in divide-and-conquer manner. Firstly, images are modelled as graph nodes, with edges are retrieved from geometric information after feature matching. Then images are divided into independent clusters by a image clustering algorithm, and followed by a subgraph expansion step, the connection and completeness of scenes are enhanced by walking along a maximum spanning tree, which is utilized to construct overlapping images between clusters. Secondly, Image clusters are distributed into servers to execute SfM in parallel mode. Thirdly, after local reconstructions complete, we construct a minimum spanning tree to find accurate similarity transformations. Then the minimum spanning tree is transformed into a Minimum Height Tree to find a proper anchor node, and is further utilized to prevent error accumulation. We evaluate our approach on various kinds of datasets and our approach shows superiority over the state-of-the-art in accuracy and efficiency. Our algorithm is open-sourced in https://github.com/AIBluefisher/GraphSfM.
Creating a Chatbot for and with Migrants

Information portals are usually created to support the integration of migrants into a host country. However, the information-seeking process can be exhausting, cumbersome and even confusing for migrants as they must cope with time-consuming information overload while searching desired information from lists of documents. Chatbots are easy-to-use, natural, and intuitive, and thus could support information-seeking. There is a lack of research that engages and empowers migrants and other stakeholders as co-design participants in chatbot development. We explored how migrants can be empowered in designing a chatbot that supports their social integration. Using a co-design approach, we conducted a series of activities with migrants and other stakeholders (i.e., online questionnaires, empathy probes, surveys, and co-design workshops) to first understand their expectations regarding chatbots, and then co-design a personality-driven chatbot. We found that chatbot personality can drive co-designing a chatbot as design goals, design directions, and design criteria.
Multi-view fringe projection system for surface topography measurement during metal powder bed fusion

Metal powder bed fusion (PBF) methods need in-process measurement methods to increase user confidence and encourage further adoption in high-value manufacturing sectors. In this paper, a novel measurement method for PBF systems is proposed that uses multi-view fringe projection to acquire high-resolution surface topography information of the powder bed. Measurements were made using a mock-up of a commercial PBF system to assess the system's accuracy and precision in comparison to conventional single-view fringe projection techniques for the same application. Results show that the multi-view system is more accurate, but less precise, than single-view fringe projection on a point-by-point basis. The multi-view system also achieves a high degree of surface coverage by using alternate views to access areas not measured by a single camera.
Effects of Intelligent Tutoring Systems (ITS) on Personalized Learning (PL)

With the advancements of technological solutions and the changes in human society has brought personalized learning into the limelight.A major technological solution has stream rolled personalized learning around the world in the development and advancements of Intelligent tutoring systems.The review of previous researches and in-depth analysis of several studies have proved that an intelligent tutoring system has made a positive impact on personalized learning, bringing some visible contributions in enhancing the performance of students and providing better time management.This research explores and unveils what personalized learning is all about and the role of an intelligent tutoring system in personalized learning.The work also covers how intelligent tutoring system has enhanced the performance of students, reduced cost for training institutes and educational system.The data in this research was collected through several means ranging from Internet research, one-on-one interviews, observations, and Educational Focus groups.Through the research methods, theoretical and empirical data were gathered.For interviews, data was effectively analyzed using content analysis techniques.The research work concludes with acknowledgment of the effects of intelligent tutoring system on personalized learning.
A binary social spider algorithm for uncapacitated facility location problem

In order to find efficient solutions to real complex world problems, computer sciences and especially heuristic algorithms are often used. Heuristic algorithms can give optimal solutions for large scale optimization problems in an acceptable period. Social Spider Algorithm (SSA), which is a heuristic algorithm created on spider behaviors are studied. The original study of this algorithm was proposed to solve continuous problems. In this paper, the binary version of the Social Spider Algorithm called Binary Social Spider Algorithm (BinSSA) is proposed for binary optimization problems. BinSSA is obtained from SSA, by transforming constant search space to binary search space with four transfer functions. Thus, BinSSA variations are created as BinSSA1, BinSSA2, BinSSA3, and BinSSA4. The study steps of the original SSA are re-updated for BinSSA. A random walking schema in SSA is replaced by a candidate solution schema in BinSSA. Two new methods (similarity measure and logic gate) are used in candidate solution production schema for increasing the exploration and exploitation capacity of BinSSA. The performance of both techniques on BinSSA is examined. BinSSA is named as BinSSA(Sim&Logic). Local search and global search performance of BinSSA is increased by these two methods. Three different studies are performed with BinSSA. In the first study, the performance of BinSSA is tested on the classic eighteen unimodal and multimodal benchmark functions. Thus, the best variation of BinSSA and BinSSA(Sim&Logic) is determined as BinSSA4(Sim&Logic). BinSSA4(Sim&Logic) has been compared with other heuristic algorithms on CEC2005 and CEC2015 functions. In the second study, the uncapacitated facility location problems (UFLPs) are solved with BinSSA(Sim&Logic). UFL problems are one of the pure binary optimization problems. BinSSA is tested on low-scaled, middle-scaled, and large-scaled fifteen UFLP samples and obtained results are compared with eighteen state-of-art algorithms. In the third study, we solved UFL problems on a different dataset named M* with BinSSA(Sim&Logic). The results of BinSSA(Sim&Logic) are compared with the Local Search (LS), Tabu Search (TS), and Improved Scatter Search (ISS) algorithms. Obtained results have shown that BinSSA offers quality and stable solutions.
A Coarse-to-Fine Two-Stage Attentive Network for Haze Removal of Remote Sensing Images

In many remote sensing (RS) applications, haze seriously degrades the quality of optical RS images and even brings inconvenience to the following high-level visual tasks such as RS detection. In this letter, we address this challenge by designing a first-coarse-then-fine two-stage dehazing neural network, named FCTF-Net. The structure is simple but effective: the first stage of image dehazing extracts multiscale features through the encoder–decoder architecture and, therefore, allows the second stage of dehazing for better refining the results of the previous stage. In addition, we combine the channel attention mechanism with the basic convolution block, considering that different channel characteristics contain entirely different weighting information, to effectively deal with irregular distribution of haze in RS images. Owing to the scarcity of various and quality hazy RS data sets, we adopt two different synthesis methods to generate large-scale image pairs for uniform and nonuniform hazy images. This two-stage network, when trained in an end-to-end fashion, yields the state-of-the-art performances on both the synthetic data sets and real-world images with more visually pleasing dehazed results. Both the synthetic data set and the code are publicly available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/cxtalk/FCTF-Net</uri> .