Reviewerly/exHarmony huggingface dataset:
“What Can I Cook with these Ingredients?” - Understanding Cooking-Related Information Needs in Conversational Search

As conversational search becomes more pervasive, it becomes increasingly important to understand the users’ underlying information needs when they converse with such systems in diverse domains. We conduct an in situ study to understand information needs arising in a home cooking context as well as how they are verbally communicated to an assistant. A human experimenter plays this role in our study. Based on the transcriptions of utterances, we derive a detailed hierarchical taxonomy of diverse information needs occurring in this context, which require different levels of assistance to be solved. The taxonomy shows that needs can be communicated through different linguistic means and require different amounts of context to be understood. In a second contribution, we perform classification experiments to determine the feasibility of predicting the type of information need a user has during a dialogue using the turn provided. For this multi-label classification problem, we achieve average F1 measures of 40% using BERT-based models. We demonstrate with examples which types of needs are difficult to predict and show why, concluding that models need to include more context information in order to improve both information need classification and assistance to make such systems usable.
Performance improvement and emission reduction of gasoline engine by adding hydrogen gas into the intake manifold

Reducing the emission pollution related to fossil fuel combustion is gaining increasing interest worldwide and the researcher did an intensive effort to reduce the dependency on fossil fuel. This work conducted an experimental study on the effect of the hydroxyl gas (HHO) produced by water electrolysis on the spark ignition engines performance. Experimental setup and procedures are performed in internal combustion laboratory at variable speeds. Different parameters like brake power, brake specific fuel consumption, thermal efficiency and emission gasses from the exhaust, were taken under consideration. It was found that brake power increased from 1059 W to 1186 W at engine speed 2600 rpm when 3 L/min of HHO added in the intake manifold and increased from 1198.2 to 1413 when the engine speed 2400 rpm. It has been observed that a significant reduction in specific fuel consumption from 968 for pure gasoline to 792 g/kW.hr at the brake power 314 W and from 506 to 365 g/kW.hr. The thermal efficiency improved from 16.93% for pure gasoline to 23.48 % by adding 3 L/min of HHO , this improvement can be attributed as a result of wide range of flame propagation and fast flame speed of hydrogen, while carbon monoxide, hydrocarbons significantly reduced. HC reduced from 240 to 215 PPM at 2600 rpm whereas CO decreased from 3.9 to 2.7 % volume. Therefore, HHO is recommended as promising substitute fuel. Since the addition of HHO gas to the fuel increased the combustion temperature, NOx emissions were slightly increased.
Mu-Net: Multi-Path Upsampling Convolution Network for Medical Image Segmentation

Medical image segmentation plays an important role in clinical diagnosis, quantitative analysis, and treatment process. Since 2015, U-Net-based approaches have been widely used for medical image segmentation. The purpose of the U-Net expansive path is to map low-resolution encoder feature maps to full input resolution feature maps. However, the consecutive deconvolution and convolutional operations in the expansive path lead to the loss of some high-level information. More high-level information can make the segmentation more accurate. In this paper, we propose MU-Net, a novel, multi-path upsampling convolution network to retain more high-level information. The MU-Net mainly consists of three parts: contracting path, skip connection, and multi-expansive paths. The proposed MU-Net architecture is evaluated based on three different medical imaging datasets. Our experiments show that MU-Net improves the segmentation performance of U-Net-based methods on different datasets. At the same time, the computational efficiency is significantly improved by reducing the number of parameters by more than half.
Predicting STC Customers’ Satisfaction Using Twitter

The telecom field has changed accordingly with the emergence of new technologies. This is the case with the telecom market in Saudi Arabia, which expanded in 2003 by attracting new investors. As a result, the Saudi telecom market became a viable market <xref ref-type="bibr" rid="ref1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[1]</xref> . The prevalence of mobile voice service among the population in Saudi Arabia for that, this research aims at mining Arabic tweets to measure customer satisfaction toward Telecom company in Saudi Arabia. This research is a use case for the Saudi Telecom Company (STC) in Saudi Arabia. The contribution of this study will be capitalized as recommendations to the company, based on monitoring in real-time their customers’ satisfaction on Twitter and from questionnaire analysis. It is the first work to evaluate customers’ satisfaction with telecommunications (telecom) company in Saudi Arabia by using both social media mining and a quantitative method. It has been built by a corpus of Arabic tweets, using a Python script searching for real-time tweets that mention Telecom company using the hashtags to monitor the latest sentiments of Telecom customers continuously. The subset is 20 000 tweets that are randomly selected from the dataset, for training the machine- classifier. In addition, we have done the experimented using deep learning network. The results show that the satisfaction for each service ranges between 31.50% and 49.25%. One of the proposed recommendations is using 5G to solve the “internet speed” problem, which showed the lowest customer satisfaction, with 31.50%.This article’s main contributions are defining the traceable measurable criteria for customer satisfaction with telecom companies in Saudi Arabia and providing telecom companies’ recommendations based on monitoring real-time customers’ satisfaction through Twitter.
Deep Learning-Based Cancer Detection-Recent Developments, Trend and Challenges

Cancer is one of the most critical diseases that has caused several deaths in today's world.In most cases, doctors and practitioners are only able to diagnose cancer in its later stages.In the later stages, planning cancer treatment and increasing the patient's survival rate becomes a very challenging task.Therefore, it becomes the need of the hour to detect cancer in the early stages for appropriate treatment and surgery planning.Analysis and interpretation of medical images such as MRI and CT scans help doctors and practitioners diagnose many diseases, including cancer disease.However, manual interpretation of medical images is costly, time-consuming and biased.Nowadays, deep learning, a subset of artificial intelligence, is gaining increasing attention from practitioners in automatically analysing and interpreting medical images without their intervention.Deep learning methods have reported extraordinary results in different fields due to their ability to automatically extract intrinsic features from images without any dependence on manually extracted features.This study provides a comprehensive review of deep learning methods in cancer detection and diagnosis, mainly focusing on breast cancer, brain cancer, skin cancer, and prostate cancer.This study describes various deep learning models and steps for applying deep learning models in detecting cancer.Recent developments in cancer detection based on deep learning methods have been critically analysed and summarised to identify critical challenges in applying them for detecting cancer accurately in the early stages.Based on the identified challenges, we provide a few promising future research directions for fellow researchers in the field.The outcome of this study provides many clues for developing practical and accurate cancer detection systems for its early diagnosis and treatment planning.
Partner learning: A comprehensive knowledge transfer for vehicle re-identification

The intra-class variability and inter-class similarity challenges caused by diverse viewpoints, illumination, and similar appearances are crucial in Re-Identification (Re-ID). Previous vehicle Re-ID methods propose to mine more discriminate and fine-grained clues for alleviating the problem, which costs extra computation and time during inference since the use of additional modules, e.g., detection modules, segmentation modules, or attention modules. We propose a multi-branch architecture to mining the discriminative and fine-grained information without additional time and computation cost during inference. Specifically, we focus on three problems: 1) how can knowledge transfer among multi-branches; 2) what knowledge should be utilized for more effective and more functional transfer; 3) where can be used as the input of multi-branches? For the first problem, we introduce a novel complementary learning scheme named partner learning which transfers the knowledge between global and local branches, and thus we only need the global branch during inference. For the second problem, we propose a hierarchical structural knowledge transfer (HSKT) approach to mine knowledge from partners in three different levels hierarchically. For the last problem, to effectively mine more fine-grained clues, we propose two local specifications: one supervised with the specification of the window area being discriminatively crucial as an expert knowledge while the other unsupervised with horizontal stripe cuts. Extensive ablation studies and experimental result discussions show the effectiveness of the proposed method.
Quantifying the Effects of Anonymization Techniques Over Micro-Databases

Micro-databases are unique datasets that contain person-specific information about individuals. Preserving the privacy of such datasets has become a cause for serious concern since this massive repository of personalized data regularly gets published in the public domain. Sanitization mechanisms are specialized techniques that provide the required privacy guarantees to the published data. The work in this article establishes an efficient framework for quantitatively estimating the effectiveness of any privacy-preservation scheme which employs the anonymization principle. In our study, we have introduced an information-theoretic metric termed as <i>Sanitization Degree</i> <inline-formula><tex-math notation="LaTeX">$(\eta)$</tex-math></inline-formula> which assigns a cumulative score in the range [0,1] for a generic anonymization process. The design of our proposed metric is based on the fundamental fact that any sanitization mechanism attempts to reduce the amount of correlated information within the database attributes while simultaneously preserving the utility of the original dataset. Furthermore, we have characterized the <i>privacy-utility</i> tradeoff associated with our model by establishing a working relationship between these two fundamental quantities. We have empirically computed the value of <inline-formula><tex-math notation="LaTeX">$\eta$</tex-math></inline-formula> for three popular anonymization models ( <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> -anonymity, <inline-formula><tex-math notation="LaTeX">$l$</tex-math></inline-formula> -diversity, and <inline-formula><tex-math notation="LaTeX">$t$</tex-math></inline-formula> -closeness) over a couple of real-life micro-databases, thereby demonstrating the practicability of our study.
Robust Path Planner for Autonomous Vehicles on Roads With Large Curvature

Path planning in real road traffic refers to the navigation of an autonomous vehicle through an obstacle-filled environment. It is crucial for the comfort, safety, and efficiency of the autonomous driving experience. It is advantageous to plan paths in the reference line-based Frenet frames rather than the Cartesian frame to decouple the 2-dimensional vehicle motion into a lateral and a longitudinal component. Path planning has two important and implicit assumptions: 1) the curvature of the reference line has to be sufficiently small, and 2) the vehicle can not be too far from the reference line. Therefore, the generated paths should follow the reference line and always head in the correct direction. However, these assumptions do not always hold in real road traffic, and the generated paths may demonstrate some unintended behaviors. In this study, we discuss the necessary and sufficient requirements for the generated paths to follow the reference line and head in the correction direction, and then proposed an efficient solution for Frenet-based path planning when the assumptions are not satisfied. Experimental results on simulated driving scenarios demonstrate the efficacy of the proposed method.
Salience-Aware Face Presentation Attack Detection via Deep Reinforcement Learning

In this paper, we propose a salience-aware face presentation attack detection (SAFPAD) approach, which takes advantage of deep reinforcement learning to exploit the salient local part information in face images. Most existing deep face presentation attack detection approaches extract features from the entire image or several fixed regions. However, the discriminative information beneficial for presentation attack detection is unevenly distributed in the image due to the illumination and presentation attack instrument variation, so treating all regions equally fails to highlight the most discriminative information which is important for more accurate and robust face presentation attack detection. To address this, we propose to identify the discriminative salient parts using deep reinforcement learning and focus on them to alleviate the adverse effects of redundant information in the face images. We fuse the high-level features and the local features which guide the policy network to exploit discriminative patches and assist the classification network to predict more accurate results. We jointly train the SAFPAD model with deep reinforcement learning to generate salient locations. Extensive experiments on five public datasets demonstrate that our approach achieves very competitive performance due to the concentrated employment of salient local information.
UniQGAN: Unified Generative Adversarial Networks for Augmented Modulation Classification

Deep learning has been widely applied to automatic modulation classification (AMC), and there have been many studies on data augmentation techniques using deep generative models to improve performance. However, existing solutions need to train different models independently for each SNR, which leads to undeniable overhead. This letter presents <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">UniQGAN</i> , Unified Generative Adversarial Networks for IQ constellations of various SNRs, requiring a single model training. The proposed method introduces <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">multi-conditions embedding</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">multi-domains classification</i> to leverage both conditions, i.e., modulation type and SNR. Experimental results show that UniQGAN effectively improves the AMC performance, while the training time is reduced.
Causal Interventional Training for Image Recognition

Deep learning models often fit undesired dataset bias in training. In this paper, we formulate the bias using <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">causal inference</i> , which helps us uncover the ever-elusive causalities among the key factors in training, and thus pursue the desired causal effect without the bias. We start from revisiting the process of building a visual recognition system, and then propose a structural causal model (SCM) for the key variables involved in dataset collection and recognition model: object, common sense, bias, context, and label prediction. Based on the SCM, one can observe that there are “good” and “bad” biases. Intuitively, in the image where a car is driving on a high way in a desert, the “good” bias denoting the common-sense context is the highway, and the “bad” bias accounting for the noisy context factor is the desert. We tackle this problem with a novel causal interventional training ( <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CIT</monospace> ) approach, where we control the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">observed</i> context in each object class. We offer theoretical justifications for <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CIT</monospace> and validate it with extensive classification experiments on CIFAR-10, CIFAR-100 and ImageNet, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g.</i> , surpassing the standard deep neural networks ResNet-34 and ResNet-50, respectively, by 0.95% and 0.70% accuracies on the ImageNet. Our code is open-sourced on the GitHub <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/qinwei-hfut/CIT</uri> .
From Collaborative Habituation to Everyday Togetherness: A Long-Term Study of Use of the Messaging Kettle

We present a long-term study of use of the Messaging Kettle, an Internet of Things (IOT) research prototype that augments an everyday kettle with both sensing and messaging capability and a beautiful light display in order to investigate connecting geographically distant loved ones to their family through the routine of boiling the kettle. Connection at a distance has been of sustained interest to the CHI community, and the social connection of older people is of increasing importance in recognition of ageing populations globally. However, very few novel designs in this domain have been investigated in situ or over the long term to examine whether their use sustains, and if so, how they impact communication in a relationship. The Messaging Kettle was trialled with four pairs of dispersed older mothers and adult daughters over timeframes that lasted between two months to more than two years. We observed the phenomenon of collaborative habituation wherein each party creatively made the technology work for them both through a combination of the gradual transformation of their everyday practices, arrangements, and living. Through developing these joint practices over time, participants expressed feelings of everyday togetherness that nurture their relationship at a distance. Three of the four couples continued to use the prototype for years, beyond the initial trial. We reflect on the artful integration of features of the Messaging Kettle and the way in which these features supported collaborative habituation . We also reflect on lessons and implications for the design of such relational technologies.
A Semi-Fragile Reversible Watermarking for Authenticating 3D Models Based on Virtual Polygon Projection and Double Modulation Strategy

Aiming to reduce the embedding distortion and improve tampering location precision of reversible watermarking for authenticating three-dimensional(3D) models, a semi-fragile reversible watermarking based on virtual polygon projection and double modulation strategy is proposed. During the embedding, it first constructs virtual adjacent vertices for each vertex and obtains a corresponding virtual polygon, and then a watermark is generated according to the projection value of the current vertex on the corresponding polygon. For each vertex, double modulation is used to move the vertex to realize watermark embedding. For the verification, it first obtains the vertex position and extracts the watermark, and then regenerates a watermark according to the restored vertex. If the extracted watermark is consistent with the regenerated one, it means that the vertex has not been tampered, and the 3D model can be lossless recovered; otherwise, the vertex is tampered. Experimental results and analysis show that the proposed scheme outperforms the existing methods in embedding distortion and tampering location precision. It has potential application in the integrity authentication of 3D models.
Cross-Domain Security and Interoperability in Internet of Things

The Internet of Things advancements has enabled smart city scenarios worldwide. A tight nit communication, on the one hand, is required for device management and monitoring, but on the other hand, it has raised risks in information exchange in cross-domain scenarios. Keeping in view the above issues, this article considered well-known cross-domain access control protocols, i.e., Shibboleth, xDAuth, and OAuth to ensure user’s security. And, the existing work focused specifically on their implementations without verifying if they are effective for the security scenarios. We aim to verify their claim of fulfilling the claimed security requirements or not. We did a Cyber Attack Analysis to highlight the possible security attacks in smart city scenarios. Then, the protocols are verified against those issues to critically analyze if they are the best fit for the said scenario. The Z3-solver and Satisfiability Modulo Theories Library have been in use for protocol verification purposes. Results reveal that the protocol models are functioning correctly in terms of confidentiality, availability, and integrity. Verifying these protocols in terms of other security properties is part of our future work.
Using the ARCS model to improve undergraduates’ perceived information security protection motivation and behavior

The ARCS (attention, relevance, confidence, and satisfaction) model was used to design an information security (InfoSec) course, and its effects on students' perceived InfoSec protection motivation and their InfoSec behavior were explored. A quasi-experimental design was adopted in this research. The participants were 147 undergraduates who took liberal arts courses in a university in southern Taiwan. There were 68 students in the experimental group and 79 in the control group. The specific teaching strategies of the ARCS model based on the Protection Motivation Theory were used to explore students' learning outcomes. The results of the study revealed that after attending the course that applied the ARCS model, the experimental group students outperformed their counterparts in terms of perceived InfoSec protection motivation (perceived severity, vulnerability, response efficacy, self-efficacy, and response costs). Meanwhile, the experimental group students’ self-report frequencies of problematic InfoSec behavior were lower than those of the control group students. These findings imply that InfoSec education using the ARCS model in the liberal arts education courses of universities has potential.
Optimizing Registration Based Encryption

The recent work of Garg et al. from TCC'18 introduced the notion of registration based encryption (RBE). The principal motivation behind RBE is to address the key escrow issue of identity based encryption (IBE), where an IBE authority is trusted to generate private keys for all users in the system. Although RBE has excellent asymptotic properties, it is currently impractical; in our estimate, ciphertext size would be about 11 TB in an RBE deployment supporting 2 billion users.