Reviewerly/exHarmony huggingface dataset:
CBA-Detector: A Self-Feedback Detector Against Cache-Based Attacks

Cloud computing is convenient to provide adequate resources for tenants. However, since multiple tenants share the underlying hardware resources, malicious tenants can use the shared processor to launch cache-based attacks. Such attacks can help malicious tenants steal private data of other tenants bypassing isolation mechanisms provided by the system, resulting in information leakage. Moreover, Spectre and Meltdown vulnerabilities can even extract memory contents arbitrarily with the help of cache attacks. Therefore, cache-based attacks pose a serious threat to the security of cloud platforms. To defeat such attacks, many detection methods have been proposed. However, most methods induce high false positives because they completely rely on the hardware performance counters (HPCs) and detect attacks with static criteria. To solve this problem, this article proposes a self-feedback detector named CBA-Detector to detect cache-based attacks in real time. Specifically, CBA-Detector first uses machine learning technologies to create models for identifying suspicious programs with abnormal hardware behaviors, then analyzes suspicious programs from the instruction level to identify real attacks and provide feedback. Based on the feedback, the models can be updated to further improve their detection accuracy. As our experiments show, CBA-Detector can accurately identify cache-based attacks in real time and introduces a little overhead. Besides, the misjudgment rate decreases with the running time.
Phase-error-compensation-based surface recovery algorithm using spectrum selection for white light interferometry

White light interferometry is a well-established surface recovery technique. In this paper, a white light signal processing algorithm based on phase error compensation using spectrum selection is proposed. The derived nonlinear phase distribution from the correlogram is modeled as the combination of random errors and systemic deviations. By developing a new, to the best of our knowledge, recovery algorithm, the phase noise can be separated from the linear map and significantly attenuated. Based on the proposed algorithm, the spectrum features of white light LEDs and halogen lamps are investigated in detail. The inner products defined by three selected points are employed to generate a coefficient to evaluate the linearity of an unwrapped phase map within a certain spectrum region. The optimal spectrum range corresponding to the best measurement performance can then be located where the coefficient approximates 1 and the spectrum energy stays relatively high. The simulations are carried out under different levels of SNR and scan step noises, which show that the new method can effectively reduce additional disturbance from the recovered topography. In experiments, the system with the proposed method is first calibrated by a step height standard (VLSI, <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mn>182.7</mml:mn> </mml:mrow> <mml:mspace width="thickmathspace" /> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mo>±<!-- ± --></mml:mo> </mml:mrow> <mml:mspace width="thickmathspace" /> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mn>2.0</mml:mn> </mml:mrow> <mml:mspace width="thickmathspace" /> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mi mathvariant="normal">n</mml:mi> <mml:mi mathvariant="normal">m</mml:mi> </mml:mrow> </mml:math> ) with the repeatability of 0.44 nm. A silicon wafer and three roughness standards are also tested to further verify the robustness of the new method.
UX and Experience Design: From Screen to 3D Space

This chapter will break down the UX elements that define the spatial nature of XR. It will look at the evolution of the 3D design space, its unique characteristics and features, to review the potential UX challenges by empathizing with the user. The chapter will narrow down a strategy to apply UX design techniques to a digital XR product, taking into account the 3D object characteristics of its components as well as considering the importance of mindful design for its long-term success.
A Theory of Label Propagation for Subpopulation Shift

One of the central problems in machine learning is domain adaptation. Unlike past theoretical work, we consider a new model for subpopulation shift in the input or representation space. In this work, we propose a provably effective framework for domain adaptation based on label propagation. In our analysis, we use a simple but realistic expansion assumption, proposed in \citet{wei2021theoretical}. Using a teacher classifier trained on the source domain, our algorithm not only propagates to the target domain but also improves upon the teacher. By leveraging existing generalization bounds, we also obtain end-to-end finite-sample guarantees on the entire algorithm. In addition, we extend our theoretical framework to a more general setting of source-to-target transfer based on a third unlabeled dataset, which can be easily applied in various learning scenarios. Inspired by our theory, we adapt consistency-based semi-supervised learning methods to domain adaptation settings and gain significant improvements.
Computation of Infection Risk via Confidential Locational Entries: A Precedent Approach for Contact Tracing With Privacy Protection

To effectively mitigate the COVID-19 pandemic, various methods have been proposed to control the infection risk using mobile phone technologies. In this respect, short-range Bluetooth in mobile phones has been mostly used to detect contacts with other devices that approach within a certain range for a specific duration and to notify residents regarding potential contact with infected patients. However, the technology can only detect direct contacts and neglects various modalities of infection, which might have contributed to the pandemic worldwide. In this article, we proposed an approach that evaluates the infection risk for residents, using the locational information of their mobile phones and confidential information of infected patients. The article first outlines the proposed method, the Computation of Infection Risks via Confidential Locational Entries method. Moreover, a comparative evaluation is qualitatively and quantitatively performed against the Bluetooth method. Results highlight the advantages of the proposed method and suggest that it could work in a complementary manner with the Bluetooth method toward effective mitigation of infection risks, while protecting privacy.
Multiclass Classification with Imbalanced Datasets for Car Ownership Demand Model – Cost-Sensitive Learning

In terms of the travel demand prediction from the household car ownership model, if the imbalanced data were used to support the transportation policy via a machine learning model, it would negatively affect the algorithm training process. The data on household car ownership obtained from the study project for the expressway preparation in the Khon Kaen Province (2015) was an unbalanced dataset. In other words, the number of members of the minority class is lower than the rest of the answer classes. The result is a bias in data classification. Consequently, this research suggested balancing the datasets with cost-sensitive learning methods, including decision trees, k-nearest neighbors (kNN), and naive Bayes algorithms. Before creating the 3-class model, a k-folds cross-validation method was applied to classify the datasets to define true positive rate (TPR) for the model’s performance validation. The outcome indicated that the kNN algorithm demonstrated the best performance for the minority class data prediction compared to other algorithms. It provides TPR for rural and suburban area types, which are region types with very different imbalance ratios, before balancing the data of 46.9% and 46.4%. After balancing the data (MCN1), TPR values were 84.4% and 81.4%, respectively.
An efficient CNN model with squirrel optimizer for handwritten digit recognition

The modern way of handwritten digit recognition has various potential real-time applications like management of students' score sheets, reading the tax form, postal code reading, and cheque processing [1].Generally, diverse numeral strings are made of overlapping, segregated, connected, or wrecked digits.Segmentation, feature selection, and feature extraction of digit handwritten images with diverse numeral string lengths are analyzed by various researchers [2].
The Relationship Between Cybersickness, Sense of Presence, and the Users’ Expectancy and Perceived Similarity Between Virtual and Real Places

This paper aims to explore the impact of sense of presence and cybersickness on the users' expectancy and perceived similarity between virtual and the corresponding real environments. Two virtual reality setups were tested (non-immersive and immersive) to achieve further conclusions. This research encompassed a quantitative analysis using data collection based on questionnaires, applied to a sample of 45 participants. A virtual experience was conducted (to explore users' cybersickness and sense of presence), followed by a visit to the actual real sites (to determine the degree of perceived similarity between the virtual and the corresponding real environment and if their expectations were fulfilled). Our results show a positive correlation between the global sense of presence and perceived similarity and users' expectancy for the non-immersive VR setup. A positive correlation was also found between the global cybersickness on both perceived similarity and users' expectancy for the immersive VR setup. Implications of such results for virtual tourism are discussed.
Smart Gradient - An adaptive technique for improving gradient estimation

&lt;p style='text-indent:20px;'&gt;Computing the gradient of a function provides fundamental information about its behavior. This information is essential for several applications and algorithms across various fields. One common application that requires gradients are optimization techniques such as stochastic gradient descent, Newton's method and trust region methods. However, these methods usually require a numerical computation of the gradient at every iteration of the method which is prone to numerical errors. We propose a simple limited-memory technique for improving the accuracy of a numerically computed gradient in this gradient-based optimization framework by exploiting (1) a coordinate transformation of the gradient and (2) the history of previously taken descent directions. The method is verified empirically by extensive experimentation on both test functions and on real data applications. The proposed method is implemented in the &lt;inline-formula&gt;&lt;tex-math id="M1"&gt;\begin{document}$\texttt{R} $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt; package &lt;inline-formula&gt;&lt;tex-math id="M2"&gt;\begin{document}$ \texttt{smartGrad}$\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt; and in C&lt;inline-formula&gt;&lt;tex-math id="M3"&gt;\begin{document}$ \texttt{++} $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt;.&lt;/p&gt;
Modelling cloud service latency and availability using a deep learning strategy

Low latency and high availability in cloud services give users satisfactory response time and guarantee stability to request they make to services that are hosted in the cloud, thus increasing the usability and reliability of cloud services. On the other hand, high latencies and poor availability will cost businesses their customers due customers dissatisfaction, thus losing customers to competitors. This situation noticeable in e-commerce businesses where real-time response and decision making within seconds are critical for business service delivery. Therefore, latency and availability are important parameters in the Service Level Agreement for cloud users when choosing Cloud Services Providers (CSPs). But the challenge for businesses is that they do not have their in–house mechanism that can accurately predict the required latency and availability for their requirements. Companies only rely on the CSPs tools for estimating resource requirements, which is biased towards the CSPs business model. In this paper, we developed a deep learning algorithm that predicts the latency and availability of cloud services using real-time live data from three CSPs. We designed and implemented experiments on Amazon Web Services, Alibaba Cloud and Tencent Cloud in Beijing University of Posts and Telecommunications to run compute instances across the United States, Europe and Asia Pacific regions. In each cloud platform, five servers were used that resulted in 30,815,100 invocations of http and ping operations for 6 weeks. The algorithm used the data on hourly, daily and weekly basis as historical network data to predict latency and availability. We used MATLABs deep learning toolbox for the implementation of our algorithm and the results showed that the prediction is usually above 90% accurate as compared with the data obtained. The results also revealed that latency performance depends on the locations of users and the availability depends on number of availability zones used.
Detecting Fake Drugs using Blockchain

The existing supply chain for the pharmaceutical industry is obsolete and lacks clear visibility over the entire system. Moreover, the circulation of counterfeit drugs in the market has increased over the years. According to the WHO report, around 10.5% of the medicinal drugs in lower / middle income countries are fake and such drugs may pose serious threats to public health, sometimes leading to death. Keeping these threats in mind, in this paper, we propose a blockchain-based model to track the movement of drugs from the industry to the patient and to minimize the chances of a drug being counterfeit. The reasons for using blockchain technology in our work include its immutability property and easy tracking of an entity in the blockchain. Through this proposed model, the manufacturer would be able to upload the details corresponding to a drug, after which it will be sent for approval to the Government. Thereafter, hospitals and pharmacies, based upon their requirements, can request the approved drugs. In the future, if a patient wants some medication, then he or she has to request it on the blockchain network. The request will be sent to the nearest hospital/pharmacy and thereafter, the patient can collect the medication. To implement this model, we have used Hyperledger fabric due to the presence of many auto-implemented features in it. Our implementation of the proposed blockchain based model highlights that the model can successfully detect any drug being counterfeit. This will be beneficial for the users getting affected with counterfeit drugs. Moreover, with the proposed model, we can also track the movement of the drug beginning from the manufacturer right up to the patient consuming that drug. Index Terms: Blockchain, Counterfeit Drugs, Drugs Tracking, Fake Medicines, Health Care.
Looking Back From the Future: Perspective Taking in Virtual Reality Increases Future Self-Continuity

In the current study, we tested a novel perspective-taking exercise aimed at increasing the connection participants felt toward their future self, i.e., future self-continuity. Participants role-played as their successful future self and answered questions about what it feels like to become their future and the path to get there. The exercise was also conducted in a virtual reality environment and in vivo to investigate the possible added value of the virtual environment with respect to improved focus, perspective-taking, and effectiveness for participants with less imagination. Results show that the perspective taking exercise in virtual reality substantially increased all four domains of future self-continuity, i.e., connectedness, similarity, vividness, and liking, while the in vivo equivalent increased only liking and vividness. Although connectedness and similarity were directionally, but not significantly different between the virtual and in vivo environments, neither the focus, perspective taking, or individual differences in imagination could explain this difference—which suggests a small, but non-significant, placebo effect of the virtual reality environment. However, lower baseline vividness in the in vivo group may explain this difference and suggests preliminary evidence for the dependency of connectedness and similarity domains upon baseline vividness. These findings show that the perspective taking exercise in a VR environment can reliably increase the future self-continuity domains.
Design and Evaluation of a Virtual Training Environment for Industrial Assembly Tasks

Due to its interactive and intuitive nature, Virtual Reality has great potential for training of industrial assembly procedures. Training in a virtual environment is potentially easier, safer and cheaper than real-world training and it allows fast switching between training scenarios. A lot of research has been done in this area, mostly conducting evaluations in laboratory settings with abstract use cases. This paper describes our implementation of a virtual training application, which was developed for a real, complex assembly in cooperation with an industry partner. The application features a guided training, which allows users to practice the assembly of complex products step by step in a virtual environment. An interactive exploded view enables the exploration of individual parts in context to the model. In addition, a sectional view tool reveals the internal structure of an assembly. The application offers collaborative tools for trainers to monitor and support the progress. A content authoring tool allows the creation and modification of training scenarios. First feedback from a qualitative study shows positive feedback from users. They appreciated the intuitive use of visual and haptic feedback for interaction. Our tests also reveals that some steps are challenging to train virtually and therefore certain simplifications are necessary. VR will not replace traditional training, but the tools described in this paper may provide a useful addition to current solutions in industry and learning factories.
A Bunched Logic for Conditional Independence

Independence and conditional independence are fundamental concepts for reasoning about groups of random variables in probabilistic programs. Verification methods for independence are still nascent, and existing methods cannot handle conditional independence. We extend the logic of bunched implications (BI) with a non-commutative conjunction and provide a model based on Markov kernels; conditional independence can be directly captured as a logical formula in this model. Noting that Markov kernels are Kleisli arrows for the distribution monad, we then introduce a second model based on the powerset monad and show how it can capture join dependency, a non-probabilistic analogue of conditional independence from database theory. Finally, we develop a program logic for verifying conditional independence in probabilistic programs.
A Scrum-based framework for new product development in the non-software industry

Scrum is an iterative and incremental development framework that, nowadays, is mainly used for software development. In this work, a Scrum-Based New Product Development (SBNPD) approach is introduced. This new framework stems from the basis of Scrum and incorporates tools suitable for being applied to the design and development of physical products. The proposed approach implies that feedback from early adopters is to be gathered throughout the whole project, thereby driving to validated learning within a co-creation process. A case study illustrates how the SBNPD framework is applied for the development of a physical product within the dental industry.
Synthetic Simulated Environments

In this chapter, we proceed from datasets of static synthetic images, either prerendered or procedurally generated, to entire simulated environments that can be used either to generate synthetic datasets on the fly or provide learning environments for reinforcement learning agents. We discuss datasets and simulations for outdoor environments (mostly for autonomous driving), indoor environments, and physics-based simulations for robotics. We also make a special case study of datasets for unmanned aerial vehicles and the use of computer games as simulated environments.
Partial-Label and Structure-constrained Deep Coupled Factorization Network

In this paper, we technically propose an enriched prior guided framework, called Dual-constrained Deep Semi-Supervised Coupled Factorization Network (DS2CF-Net), for discovering hierarchical coupled data representation. To extract hidden deep features, DS2CF-Net is formulated as a partial-label and geometrical structure-constrained framework. Specifically, DS2CF-Net designs a deep factorization architecture using multilayers of linear transformations, which can coupled update both the basis vectors and new representations in each layer. To enable learned deep representations and coefficients to be discriminative, we also consider enriching the supervised prior by joint deep coefficients-based label prediction and then incorporate the enriched prior information as additional label and structure constraints. The label constraint can enable the intra-class samples to have same coordinate in feature space, and the structure constraint forces the coefficients in each layer to be block-diagonal so that the enriched prior using the self-expressive label propagation are more accurate. Our network also integrates the adaptive dual-graph learning to retain the local structures of both data and feature manifolds in each layer. Extensive experiments on image datasets demonstrate the effectiveness of DS2CF-Net for representation learning and clustering.
Boosting Unconstrained Face Recognition with Auxiliary Unlabeled Data

In recent years, significant progress has been made in face recognition, which can be partially attributed to the availability of large-scale labeled face datasets. However, since the faces in these datasets usually contain limited degree and types of variation, the resulting trained models generalize poorly to more realistic unconstrained face datasets. While collecting labeled faces with larger variations could be helpful, it is practically infeasible due to privacy and labor cost. In comparison, it is easier to acquire a large number of unlabeled faces from different domains, which could be used to regularize the learning of face representations. We present an approach to use such unlabeled faces to learn generalizable face representations, where we assume neither the access to identity labels nor domain labels for unlabeled images. Experimental results on unconstrained datasets show that a small amount of unlabeled data with sufficient diversity can (i) lead to an appreciable gain in recognition performance and (ii) outperform the supervised baseline when combined with less than half of the labeled data. Compared with the state-of-the-art face recognition methods, our method further improves their performance on challenging benchmarks, such as IJBB, IJB-C and IJB-S.