Reviewerly/exHarmony huggingface dataset:
Data-Driven Rendering of Motion Effects for Walking Sensations in Different Gaits

Motion effects are a vital component in 4D interactive applications, where special physical effects, such as motion, vibration, and wind, are provided with audiovisual stimuli. In 4D films and VR games, the scenes that show human locomotion appear frequently, and motion effects emphasizing such movements can enhance the viewers’ immersive experiences. This paper proposes a data-driven framework for automatic generation of the motion effects that provide users with walking sensations. Measurements are made using the motion sensors attached to the human body during locomotion in different gaits, e.g., walking, running, and stumping. The captured data are processed and converted to multiple degree-of-freedom commands to a motion platform. We demonstrate that the data-driven motion commands can be represented in a greatly lower-dimensional space by principal component analysis. This finding leads to an algorithm for the synthesis of new motion commands that can elicit the target gait's walking sensations. The perceptual performance of our method is validated by two user studies. This work contributes to investigating the feasibility of mimicking walking sensations using a motion platform based on human locomotion data and developing an automatic generation algorithm of motion effects conveying the impressions of different gaits.
Set-Valued Support Vector Machine with Bounded Error Rates

This article concerns cautious classification models that are allowed to predict a set of class labels or reject to make a prediction when the uncertainty in the prediction is high. This set-valued classification approach is equivalent to the task of acceptance region learning, which aims to identify subsets of the input space, each of which guarantees to cover observations in a class with at least a predetermined probability. We propose to directly learn the acceptance regions through risk minimization, by making use of a truncated hinge loss and a constrained optimization framework. Collectively our theoretical analyses show that these acceptance regions, with high probability, satisfy simultaneously two properties: (a) they guarantee to cover each class with a noncoverage rate bounded from above; (b) they give the least ambiguous predictions among all the acceptance regions satisfying (a). An efficient algorithm is developed and numerical studies are conducted using both simulated and real data. Supplementary materials for this article are available online.
Multi-Magnification Image Search in Digital Pathology

This paper investigates the effect of magnification on content-based image search in digital pathology archives and proposes to use multi-magnification image representation. Image search in large archives of digital pathology slides provides researchers and medical professionals with an opportunity to match records of current and past patients and learn from evidently diagnosed and treated cases. When working with microscopes, pathologists switch between different magnification levels while examining tissue specimens to find and evaluate various morphological features. Inspired by the conventional pathology workflow, we have investigated several magnification levels in digital pathology and their combinations to minimize the gap between AI-enabled image search methods and clinical settings. The proposed searching framework does not rely on any regional annotation and potentially applies to millions of unlabelled (raw) whole slide images. This paper suggests two approaches for combining magnification levels and compares their performance. The first approach obtains a single-vector deep feature representation for a digital slide, whereas the second approach works with a multi-vector deep feature representation. We report the search results of 20×, 10×, and 5× magnifications and their combinations on a subset of The Cancer Genome Atlas (TCGA) repository. The experiments verify that cell-level information at the highest magnification is essential for searching for diagnostic purposes. In contrast, low-magnification information may improve this assessment depending on the tumor type. Our multi-magnification approach achieved up to 11% F1-score improvement in searching among the urinary tract and brain tumor subtypes compared to the single-magnification image search.
Design of a Blockchain based WMSN Model for Secure and Effective Health Monitoring System Deployment

Wireless medical sensor networks (WMSN) are special types of sensor networks that are suited for medical applications. These networks must guarantee high security with low latency to store and retrieve data with high trust levels. To meet these constraints various security measures are incorporated by WMSN designers that include data encryption, hashing, steganography, etc. These models provide high security but reduce the quality of service (QoS) due to their computational complexity. Moreover, these models have limited trustability and computational performance, due to their non-tamper proof and centralized processing characteristics. To improve the security and QoS performance of WMSN deployments, this text proposes a novel blockchain-based model that is based on a novel Proof-of-Trust and Speed (PoTS) based consensus method. This is a new consensus model developed solely for the design of the proposed WMSN, which is used to monitor the health status of patients in real-time with high security. The proposed model is capable of providing high trust, complete traceability, low delay, and distributed tamper-proof transactions. The proposed model evaluates temporal node performance and selects miner nodes that have higher trust and faster speeds. Nodes that showcased lower communication delay, with minimum packet drops and lower energy consumption were selected as miner nodes. These values assisted in the evaluation of temporal trust for every WMSN communication. Due to the incorporation of temporal trust and speed values, the model can reduce the number of redundant miner selection steps, thereby assisting in the design of an improved WMSN model. The model was tested on a wide variety of network scenarios, and it was observed to be 18% faster, with 5% lower energy consumption, and 8% better throughput than state-of-the-art methods. Furthermore, the model was also able to mitigate Sybil, Masquerading, and Spoofing attacks, which makes it highly deployable for a wide variety of network scenarios.
A Blockchain and AI Based Vaccination Tracking Framework for Coronavirus (COVID-19) Epidemics

The Coronavirus disease, i.e. (COVID-19) pandemic outbreaks has inevitably lead to a corresponding movement restrictive measure by the members of both central and state government leadership. The current limitation of healthcare systems cannot predict the COVID-19 outbreaks and vaccination drive. The need of the hour is to use Blockchain Technology for the construction of immutable ledger. In this paper a Blockchain and Artificial Intelligence (AI) enabled COVID-19 vaccine tracking system has been proposed. The proposed system is designed using the Ethereum Virtual Machine (EVM), decentralized storage by Inter Planetary File System (IPFS) and implemented using Truffle and Ganache Tool. The smart contract interaction among the entities is developed with Drizzle and the front-end using ReactJS . The performance of the proposed framework has been presented with Keccack 256 transaction hash, the total number of transactions, gas consumed by each contract. Additionally the performance parameters such as latency, throughput, traffic in, out, CPU, and memory utilization of the blockchain framework are also calculated. The artificial neural network (ANN) has been used to classify the vaccination group. Such an attempt is a worthwhile addition to the state of the art as evident from the results presented herein. The proposed framework provides a way for vaccinated members of the community to be tracked and indexed so as to avail the benefits, for example, less restrictive movements nationally, internationally, or simply in their own respective communities. This paper leverages the advantages of Blockchain Technology to obtain a reliable and accurate way to facilitate the entire tracking process.
State control strategy of open quantum systems by reverse engineering: via homodyne-based feedback control

Quantum state transition is the core content of quantum control. This paper presents a control scheme of the state evolution path based on feedback control and reverse engineering design. To achieve high-precision state transition, we first adopt a homodyne-based feedback control strategy to form a closed-loop system. Then, based on the idea of reverse engineering design, the state evolution path of the system is planned and designed by considering the influence of environment decoherence on the state evolution. Numerical simulation results show that the control pulse based on planning design can drive the system from any initial state to the desired target state, and the coherence of the system is effectively protected during state transition.
Iterative Mixed Method Approach to B2B SaaS User Personas

User persona research has primarily been done on Business-to-Consumer (B2C) products, generated hybridly with interviews or surveys or purely quantitatively through clickstream data, often to serve sales / marketing teams. However, the application of these approaches in different circumstances to this is still relatively novel. How can we segment users in a way that is both helpful and accurate for a Business-to-Business (B2B) Software-as-a-Service (SaaS) product and its product development stakeholders, for a product with "imperfect" / obfuscated product analytics data and disjointed user journeys? We performed an iterative, qualitative and mixed quantitative approach to user personas that utilized survey data, user interviews, and preprocessed / manipulated web analytics data to provide a complete picture of the user. Survey data provided user background and insight into who to recruit while interviews provided insight into user journeys, personalities, and pain points. Using dimensionality reduction and an iterative clustering approach informed by survey and interview insights, we generated clusters based on users' time per page and clicks per feature data from our web analytics. Diving into the data behind each cluster and triangulating with interview and survey data enabled us to identify six distinct user personas. We recommend our approach to user segmentation, an as yet unexplored combination of qualitative and mixed quantitative methods, for the research teams of other mid-sized B2B SaaS companies as a way to address domain-specific challenges and bridge the data and user journeys gaps to paint a full, accurate picture of the user.
Inverse engineering of fast state transfer among coupled oscillators

We design faster-than-adiabatic state transfers (switching of quantum numbers) in time-dependent coupled-oscillator Hamiltonians. The manipulation to drive the process is found using a two-dimensional invariant recently proposed in S. Simsek and F. Mintert, Quantum 5 (2021) 409, and involves both rotation and transient scaling of the principal axes of the potential in a Cartesian representation. Importantly, this invariant is degenerate except for the subspace spanned by its ground state. Such degeneracy, in general, allows for infidelities of the final states with respect to ideal target eigenstates. However, the value of a single control parameter can be chosen so that the state switching is perfect for arbitrary (not necessarily known) initial eigenstates. Additional 2D linear invariants are used to find easily the parameter values needed and to provide generic expressions for the final states and final energies. In particular we find time-dependent transformations of a two-dimensional harmonic trap for a particle (such as an ion or neutral atom) so that the final trap is rotated with respect to the initial one, and eigenstates of the initial trap are converted into rotated replicas at final time, in some chosen time and rotation angle.
Dynamic Privacy Budget Allocation Improves Data Efficiency of Differentially Private Gradient Descent

Protecting privacy in learning while maintaining the model performance has become increasingly critical in many applications that involve sensitive data. Private Gradient Descent (PGD) is a commonly used private learning framework, which noises gradients based on the Differential Privacy protocol. Recent studies show that dynamic privacy schedules of decreasing noise magnitudes can improve loss at the final iteration, and yet theoretical understandings of the effectiveness of such schedules and their connections to optimization algorithms remain limited. In this paper, we provide comprehensive analysis of noise influence in dynamic privacy schedules to answer these critical questions. We first present a dynamic noise schedule minimizing the utility upper bound of PGD, and show how the noise influence from each optimization step collectively impacts utility of the final model. Our study also reveals how impacts from dynamic noise influence change when momentum is used. We empirically show the connection exists for general non-convex losses, and the influence is greatly impacted by the loss curvature.
Exploring Kolam As An Ecofeminist Computational Art Practice

In this pictorial, we present Kolam, a visual artform originating in Tamilnadu, South India, as an ecofeminist computational art practice. We provide a visual documentation of Kolam's Intangible Cultural Heritage (ICH) through eight characteristics based on existing research and authors’ personal experiences as Kolam practitioners. We begin by framing Kolam as an ecofeminist practice, highlighting cultural and ecological characteristics of Kolam as a Tamil tradition. We then illustrate evolving hybrid multimedia and contemporary technological practices that characterize Kolam as computational art. Our aim is to present a cohesive and compelling visual narrative using the artwork of authors and four contemporary Kolam practitioners to inspire creativity and highlight challenges for relational knowledge production in design and Human-Computer Interaction (HCI) research..
A Scalable Mixed Reality Platform for Remote Collaborative LEGO Design

Mixed reality (MR) is a new paradigm that merges both real and virtual worlds to create new environments and visualizations. This together with the rapid growth of wireless virtual/augmented reality devices (such as smartphones and Microsoft HoloLens) spurs collaborative MR applications that provide an interactive and immersive experience for a group of people. In this demo, we develop a scalable MR-based platform for remote collaborative LEGO design. To provide the best immersive experience, the system should provide 1) high-speed and high-resolution image rendering: the rendering should achieve screen resolution of the mobile device and at least 60 frames-per-second; 2) extremely low delay guarantees: the motion-to-display latency of each user should be below 20ms; 3) synchronization: the synchronization latency should be small enough to enable the smooth collaboration; 4) scalability: the number of users should not have a significant impact on the system performance. To achieve all these goals, we introduce a central server to facilitate user synchronization via exchanging small messages. Each user reports to the server with its LEGO design progress, which is then distributed to all other users by the server; all other users render the corresponding virtual LEGO models in its own design space. We demonstrate via real-world implementations and evaluations that: 1) our system performance (e.g., synchronization delay, frame rate) does not degrade with the increase of the number of users; 2) our developed system not only yields a motion-to-display delay of 11 ms (i.e., 90 frames per second) but also achieves a screen resolution of each user’s mobile device (e.g., 2400 × 1080 pixels for Google Pixel 6).
ARDformer: Agroforestry Road Detection for Autonomous Driving Using Hierarchical Transformer

Road detection is a crucial part of the autonomous driving system, and semantic segmentation is used as the default method for this kind of task. However, the descriptive categories of agroforestry are not directly definable and constrain the semantic segmentation-based method for road detection. This paper proposes a novel road detection approach to overcome the problem mentioned above. Specifically, a novel two-stage method for road detection in an agroforestry environment, namely ARDformer. First, a transformer-based hierarchical feature aggregation network is used for semantic segmentation. After the segmentation network generates the scene mask, the edge extraction algorithm extracts the trail's edge. It then calculates the periphery of the trail to surround the area where the trail and grass are located. The proposed method is tested on the public agroforestry dataset, and experimental results show that the intersection over union is approximately 0.82, which significantly outperforms the baseline. Moreover, ARDformer is also effective in a real agroforestry environment.
Optimized DPoS consensus strategy: Credit-weighted comprehensive election

Consensus algorithm, which allows distrust nodes to reach a consensus in the overall state of the blockchain, is one of the basic characteristics of the blockchain network. Since the delegate nodes elected in the DPoS consensus algorithm are always nodes with a large amount of currency, it usually leads to low voting activity of other nodes. To solve this problem, we propose a credit-weighted comprehensive election, codenamed as CW-DPoS. We propose that the voting weight of nodes is positively correlated with the credit value. First calculate the activity of the node based on k-shell,second calculate the weighted voting value of the node. Finally, the delegator is selected through the comprehensive method of node activity and weighted votes. The performance of the proposed method is compared with the existing consensus mechanism of DPoS, and the experimental result shows that the activity of the nodes has been improved to about 84% and selects honest nodes more fairly.
COVID-19 pandemic &amp; cyber security issues: Sentiment analysis and topic modeling approach

The purpose of this study is to understand public awareness of cybersecurity-related issues and discussions during the COVID-19 pandemic. Employees and confidential organizational data are vulnerable to cyber-attacks as a result of the pandemic, which has raised concerns about cyber security about the new normal of working from home. The public’s main sentiments and aspects related to cyber security concerns have been mined from tweets on the microblogging social media website Twitter. Sentiment Analysis and Topic Modeling techniques have been applied to understand the perspectives, emotions, and themes discussed by people. The analysis reveals people are becoming more aware of cybercrime-related concerns and are more positive than negative in combating the challenge of cybercrime. The paper also highlights the main themes revealed by Topic Modeling.
Tomographic scanning of rock joint roughness

Geotechnical logging of drillcore typically includes joint roughness due to its importance in rock mass characterisation. Here we propose digitising the measurement of roughness via a tomography scan. Image processing steps are described for relating the scan to a simple profilometric definition of roughness. The approach is amenable to directional statistical visualisation on a stereoplot.
DNN Model Architecture Fingerprinting Attack on CPU-GPU Edge Devices

Embedded systems for edge computing are getting more powerful, and some are equipped with a GPU to enable on-device deep neural network (DNN) learning tasks such as image classification and object detection. Such DNN-based applications frequently deal with sensitive user data, and their architectures are considered intellectual property to be protected. We investigate a potential avenue of fingerprinting attack to identify the (running) DNN model architecture family (out of state-of-the-art DNN categories) on CPU-GPU edge devices. We exploit a stealthy analysis of aggregate system-level side-channel information such as memory, CPU, and GPU usage available at the user-space level. To the best of our knowledge, this is the first attack of its kind that does not require physical access and/or sudo access to the victim device and only collects the system traces passively, as opposed to most of the existing reverse-engineering-based DNN model architecture extraction attacks. We perform feature selection analysis and supervised machine learning-based classification to detect the model architecture. With a combination of RAM, CPU, and GPU features and a Random Forest-based classifier, our proposed attack classifies a known DNN model into its model architecture family with 99% accuracy. Also, the introduced attack is so transferable that it can detect an unknown DNN model into the right DNN architecture category with 87.2% accuracy. Our rigorous feature analysis illustrates that memory usage (RAM) is a critical feature for such fingerprinting. Furthermore, we successfully replicate this attack on two different CPU-GPU platforms and observe similar experimental results that exhibit the capability of platform portability of the attack. Also, we investigate the robustness of the proposed attack to varying background noises and a modified DNN pipeline. Besides, we exhibit that the leakage of model architecture family information from this stealthy attack can strengthen an adversarial attack against a victim DNN model by 2×.
Learning to Generate Scene Graph from Head to Tail

Scene Graph Generation (SGG) represents objects and their interactions with a graph structure. Recently, many works are devoted to solving the imbalanced problem in SGG. However, underestimating the head predicates in the whole training process, they wreck the features of head predicates that provide general features for tail ones. Besides, assigning excessive attention to the tail predicates leads to semantic deviation. Based on this, we propose a novel SGG framework, learning to generate scene graphs from Head to Tail (SGG-HT), containing Curriculum Re-weight Mechanism (CRM) and Semantic Context Module (SCM). CRM learns head/easy samples firstly for robust features of head predicates and then gradually focuses on tail/hard ones. SCM is proposed to relieve semantic deviation by ensuring the semantic consistency between the generated scene graph and the ground truth in global and local representations. Experiments show that SGG-HT significantly alleviates the biased problem and achieves state-of-the-art performances on Visual Genome.