Reviewerly/exHarmony huggingface dataset:
Unitary equivalence classes of split-step quantum walks

In this study, we investigate the unitary equivalence of split-step quantum walks (SSQWs). Introduced by Kitagawa and generalized by Suzuki, SSQWs are two-state discrete-time quantum walks on a one-dimensional lattice. In this work, we define a new class of quantum walks that includes all SSQWs by making use of the rank conditions, which state that (1) the rank of operators representing the walk from vertex x to vertex $$x\pm 1$$ is less than or equal to 1 and that (2) the rank from vertex x to x (i.e., to itself) is less than or equal to 2. Initially, we abstractly define quantum walks in this class, then clarify by presenting the explicit form of such quantum walks. We also calculate their unitary equivalence classes and show that four real parameters are required for each vertex to parameterize the unitary equivalence classes. Further, we consider the unitary equivalence of Suzuki's SSQWs and prove that all parameters of Suzuki's SSQWs can be real. Finally, we discuss the chiral symmetry of SSQWs.
Analyzing and Improving Fault Tolerance of Learning-Based Navigation Systems

Learning-based navigation systems are widely used in autonomous applications, such as robotics, unmanned vehicles and drones. Specialized hardware accelerators have been proposed for high-performance and energy-efficiency for such navigational tasks. However, transient and permanent faults are increasing in hardware systems and can catastrophically violate tasks safety. Meanwhile, traditional redundancy-based protection methods are challenging to deploy on resource-constrained edge applications. In this paper, we experimentally evaluate the resilience of navigation systems with respect to algorithms, fault models and data types from both RL training and inference. We further propose two efficient fault mitigation techniques that achieve $2 \times$ success rate and 39% quality-of-flight improvement in learning-based navigation systems.
Standing, Walking, and Sitting Support Robot Based on User State Estimation Using a Small Number of Sensors

With the aging of the population and the consequent severe shortage of caregivers, the demand for care robots to assist the elderly is increasing. However, care robots have yet to be widely adopted owing to cost constraints and anxiety issues due to several factors. For instance, care robots are required to have higher functionality than general care devices. It is important to provide both massive power and the appropriate support for the user’s state. However, this requires more sensors to obtain detailed information for user-state estimation and more actuators for physical support, increasing the cost and risk of failure. In a system that has many sensors and operates based on detailed data, the problem of user privacy also emerges. The risk of personal information leakage and the feeling of being monitored increase user discomfort. To support standing up and prevent falling during walking, care robots are required to apply power to the user according to the user state. The position of the center of gravity (CoG) has been used for such state estimation; however, many sensors are required to determine the accurate CoG position. To reduce the number of sensors required for user state estimation, we proposed a method for calculating CoG candidates, and validated the proposed method via experiments. Previous studies have focused solely on normal standing-up motion. However, in daily activities, standing up, walking, and sitting down are a set of motions. In addition, it is not always true that the care robot user can move normally; hence, anomaly detection is beneficial in care robots. Therefore, it is important to estimate the user state considering not only standing-up motion, but also walking and sitting down, as well as any anomaly that may occur during these motions. In this study, we develop an elderly support system that can assist in standing, walking, and sitting based on user state estimation. The CoG candidate calculation method is improved for walking and stand-to-sit movements, and an anomaly detection method using CoG candidates is also proposed. The care robot is designed to be user-driven and provide support for persons with insufficient strength based on state estimation. The experiments verify that the developed system can constantly monitor the user’s state and support a series of movements, such as standing up, walking, and sitting down, with a single robot.
Path Planning of Mobile Robot Based on Adaptive Ant Colony Optimization

In order to solve the problems of slow convergence speed and poor global search ability in mobile robot path planning, an adaptive ant colony optimization algorithm (AACO) is proposed in this paper. First, in the early stage of the ant colony search, adaptive initial pheromone distribution is used to reduce the blindness of ant colony algorithm. Using adaptive pheromone factor and adaptive evaporation factor to improve the role of pheromone in different periods of convergence of ant colony algorithm. Improve the update mechanism of pheromones and use pheromone preferential limited update to reduce the redundancy of pheromones. A novel adaptive pheromone reconstruction mechanism is proposed to improve the global search capability of the ant colony algorithm. Finally, through two random environment experiments, the proposed algorithm has better path planning ability than some similar algorithms and classical ant colony algorithms.
A Keyword-Grouping Inverted Index Based Multi-Keyword Ranked Search Scheme Over Encrypted Cloud Data

With the comprehensive development of cloud computing technology, more and more enterprises and individuals tend to outsource computing, data, and other resources to the cloud service providers to save the data management cost. Since the plaintext data outsourcing in the cloud could leak users’ private information, it is highly recommended to encrypt them before outsourcing. However, it is a challenge to perform searches over encrypted cloud data. In this paper, we adopt the keyword grouping idea into the traditional inverted index and propose a keyword-grouping inverted index (KGI-index). Based on the index, we propose a privacy-preserving KGI-index based multi-keywords ranked search scheme (KMRS). To improve the search efficiency, we adopt two strategies including grouping high relevant keywords and using the complete binary tree structure to optimize the index. The security analysis and experimental result show that the proposed scheme is a privacy-preserving and efficient multi-keyword ranked search scheme over encrypted cloud data.
Data augmentation based non-parallel voice conversion with frame-level speaker disentangler

Non-parallel data voice conversion is a popular and challenging research area. The main task is to build acoustic mappings from the source speaker to the target speaker in different units (e.g., frame, phoneme, cluster, sentence). With the help of the recent high-quality speech synthesis techniques, it is possible to directly produce parallel speech using non-parallel data. This paper proposes ParaGen: a data augmentation based technique for non-parallel data voice conversion. The system consists of a speaker disentangler based text-to-speech model and a simple frame-to-frame spectrogram conversion model. The text-to-speech model takes the text and reference audio as input to produce the speech in the target speaker identity with the time-aligned local speaking style from the reference audio. The spectrogram conversion model directly converts the source spectrogram to the target speaker framewisely. The local speaking style is extracted by an acoustic encoder while the speaker identity is eliminated by a conditional convolutional disentangler. The local style encodings are time-aligned with the text encodings by the attention mechanism. The attention contexts are decoded by a conditional recurrent decoder. The experiment shows that the speaker identity of the source speech is converted to the target speaker while the local speaking style (e.g., prosody) is preserved after the augmentation. The method is compared to the augmentation model with typical statistical parameter speech synthesis (SPSS) with pre-aligned phoneme duration. The result shows that the converted speech has better speech naturalness than the SPSS system, while the speaker similarities of the converted speech are close.
Educational Applications of Immersive Technology: A Technical Report

Immersive technologies bring the physical world together with a virtual or digital reality to create new experiences. Augmented reality, virtual reality, and mixed reality are examples of immersive technologies. The technique of superimposing auditory, visual, or any other sensory data on top of the real world to improve one's experience is known as augmented reality. Unlike virtual reality, which creates its own virtual world, augmented reality adds to the one that already exists. In education, augmented reality includes elements that improve collaboration, creative skills, and problem-solving in order to better prepare pupils for the future. This paper survey research papers on augmented reality application developed for learning physics, chemistry, biology, and mathematics.
High accuracy human activity recognition using machine learning and wearable devices’ raw signals

Human activity recognition (HAR) is vital in a wide range of real-life applications such as health monitoring of olderly people, abnormal behaviour detection and smart home management. HAR systems can employ smart human-computer interfaces and be parts of active, intelligent surveillance systems. The increasing use of high-tech mobile and wearable devices, such as smart phones, smart watches and smart bands, can be the key elements in building high accuracy models, as they can provide a tremendous number of signals. This research aims to develop and test a machine learning (ML) model, which can successfully recognize a performed activity using raw signals obtained by wearable devices. Photoplethysmography – Daily Life Activities (PPG-DaLiA) dataset contains data related to 15 individuals wearing physiological and motion sensors. PPG-DaLiA was used as an input to a custom data segmentation model to obtain the respective training and testing dataset. Overall, 23 ML well-established models were employed. The weighted and the fine k-nearest neighbours, the fine Gaussian support vector machines and the bagged trees were the algorithms that achieved the best performance with a very high accuracy level.
Construction of weightwise almost perfectly balanced Boolean functions on an arbitrary number of variables

The recent FLIP cipher is an encryption scheme described by Méaux et al. at EUROCRYPT 2016. In the FLIP cipher, the input to the Boolean function used as a filter function has constant Hamming weight equal to the weight of the secret key. Weightwise perfectly balanced Boolean functions, which can be used in the FLIP cipher, only exist on 2m variables, while weightwise almost perfectly balanced Boolean functions can exist on an arbitrary number of variables. In this paper, we first introduce a class of quadratic Boolean functions on n variables, where n is a positive integer. We then propose a construction of weightwise almost perfectly balanced Boolean functions on n variables by modifying the support of the quadratic Boolean functions.
Applying the ARCS Motivation Theory for the Assessment of AR Digital Media Design Learning Effectiveness

This research adopts Keller’s ARCS motivation theory as a method to create a teaching experiment by integrating augmented reality (AR) into teaching in order to enhance learning interest and learning effectiveness in a digital media design course. The purpose of this research is to examine the application of AR in quarantine during the COVID-19 pandemic, whereby students can enhance their learning interest, learning satisfaction, and learning performance. Augmented reality acts as a tool for this research, wherein it is applied with the course of a 3D model-based interface and built-in learning contexts for the “digital media design” of the learning topics. The learning performance of the test group students was examined through the aspects of attention, relevance, confidence, and satisfaction, according to the ARCS motivation theory. According to the results of quantitative validation, experimental teaching with AR is more effective than traditional teaching methods. The learning feedback of the test group students obtained a positive result by using the AR. This research concludes with three results: firstly, integrating AR into teaching can improve students’ concentration with respect to digital media design practice; secondly, video teaching with an AR interface can help increase students’ confidence in digital media design learning; lastly, applying AR during the learning process can enhance the digital media visual effects that effectively enhance students’ self-learning abilities.
A Novel Low-Area Point Multiplication Architecture for Elliptic-Curve Cryptography

This paper presents a Point Multiplication (PM) architecture of Elliptic-Curve Cryptography (ECC) over GF(2163) with a focus on the optimization of hardware resources and latency at the same time. The hardware resources are reduced with the use of a bit-serial (traditional schoolbook) multiplication method. Similarly, the latency is optimized with the reduction in a critical path using pipeline registers. To cope with the pipelining, we propose to reschedule point addition and double instructions, required for the computation of a PM operation in ECC. Subsequently, the proposed architecture over GF(2163) is modeled in Verilog Hardware Description Language (HDL) using Vivado Design Suite. To provide a fair performance evaluation, we synthesize our design on various FPGA (field-programmable gate array) devices. These FPGA devices are Virtex-4, Virtex-5, Virtex-6, Virtex-7, Spartan-7, Artix-7, and Kintex-7. The lowest area (433 FPGA slices) is achieved on Spartan-7. The highest speed is realized on Virtex-7, where our design achieves 391 MHz clock frequency and requires 416 μs for one PM computation (latency). For power, the lowest values are achieved on the Artix-7 (56 μW) and Kintex-7 (61 μW) devices. A ratio of throughput over area value of 4.89 is reached for Virtex-7. Our design outperforms most recent state-of-the-art solutions (in terms of area) with an overhead of latency.
Personas for Better Targeted eHealth Technologies: User-Centered Design Approach

The full potential of eHealth technologies to support self-management and disease management for patients with chronic diseases is not being reached. A possible explanation for these lacking results is that during the development process, insufficient attention is paid to the needs, wishes, and context of the prospective end users. To overcome such issues, the user-centered design practice of creating personas is widely accepted to ensure the fit between a technology and the target group or end users throughout all phases of development.In this study, we integrate several approaches to persona development into the Persona Approach Twente to attain a more holistic and structured approach that aligns with the iterative process of eHealth development.In 3 steps, a secondary analysis was carried out on different parts of the data set using the Partitioning Around Medoids clustering method. First, we used health-related electronic patient record data only. Second, we added person-related data that were gathered through interviews and questionnaires. Third, we added log data.In the first step, 2 clusters were found, with average silhouette widths of 0.12 and 0.27. In the second step, again 2 clusters were found, with average silhouette widths of 0.08 and 0.12. In the third step, 3 clusters were identified, with average silhouette widths of 0.09, 0.12, and 0.04.The Persona Approach Twente is applicable for mixed types of data and allows alignment of this user-centered design method to the iterative approach of eHealth development. A variety of characteristics can be used that stretches beyond (standardized) medical and demographic measurements. Challenges lie in data quality and fitness for (quantitative) clustering.
Visual Understanding of Complex Table Structures from Document Images

Table structure recognition is necessary for a comprehensive understanding of documents. Tables in unstructured business documents are tough to parse due to the high diversity of layouts, varying alignments of contents, and the presence of empty cells. The problem is particularly difficult because of challenges in identifying individual cells using visual or linguistic contexts or both. Accurate detection of table cells (including empty cells) simplifies structure extraction and hence, it becomes the prime focus of our work. We propose a novel object-detection-based deep model that captures the inherent alignments of cells within tables and is fine-tuned for fast optimization. Despite accurate detection of cells, recognizing structures for dense tables may still be challenging because of difficulties in capturing long-range row/column dependencies in presence of multi-row/column spanning cells. Therefore, we also aim to improve structure recognition by deducing a novel rectilinear graph-based formulation. From a semantics perspective, we highlight the significance of empty cells in a table. To take these cells into account, we suggest an enhancement to a popular evaluation criterion. Finally, we introduce a modestly sized evaluation dataset with an annotation style inspired by human cognition to encourage new approaches to the problem. Our framework improves the previous state-of-the-art performance by a 2.7% average F1-score on benchmark datasets.
Missing Data Imputation for Real Time-series Data in a Steel Industry using Generative Adversarial Networks

On the verge of technology, manufacturing industries revolutionize into smart industries, which create a large amount of multivariate time-series data. However, due to sensors’ failure, extreme environment, etc., the collected data are incomplete and have missing values at several instances that result in an erroneous analysis of the data. The key to resolving this problem is data imputation, i.e., replacing the missing values with synthetic values. In this paper, we introduce a generative adversarial network (GAN) framework to generate the synthetic data pertaining to the data imputation. Over the last decade, GANs have presented excellent results to generate synthetic data for images. By following this stream of research, we consider multivariate time-series data from a steel manufacturing industry and propose a GAN-based data imputation technique. We perform several computer simulations to validate and compare the performance of the proposed GAN method with state-of-the-art data imputation techniques.
Sentiment Analysis using Improved Vader and Dependency Parsing

We know, one Act has been initiated by the Government of India in September 2020, often known as Farm Bills or Indian Agricultural Act. This act has been affected farmers in many ways and led to opposition to the bills. As a result, there is a wide area for doing sentiment on the data taken from this domain, so we are making sentiment analysis on it. On comparing different algorithms like Logistic Regression, VADER, and BERT, we could see that BERT is having more accuracy as compared to the other algorithms. But we could see that VADER is a good algorithm as they are having special qualities as compared to that of the other algorithm. So, we thought to Improve VADER (Valence Aware Dictionary and sEntiment Reasoner) sentiment analysis on Farm Bill Act. Along with this, we are doing Information Extraction on verb and analyzing sentiments on extracted phrases that are related to the verb, to get the accuracy of sentences with verb and without verb. Thus, we get the Dependency of the verb in the sentence.
MC-CIM: Compute-in-Memory With Monte-Carlo Dropouts for Bayesian Edge Intelligence

We propose MC-CIM, a compute-in-memory (CIM) framework for robust, yet low power, Bayesian edge intelligence. Deep neural networks (DNN) with deterministic weights cannot express their prediction uncertainties, thereby pose critical risks for applications where the consequences of mispredictions are fatal such as surgical robotics. To address this limitation, Bayesian inference of a DNN has gained attention. Using Bayesian inference, not only the prediction itself, but the prediction confidence can also be extracted for planning risk-aware actions. However, Bayesian inference of a DNN is computationally expensive, ill-suited for real-time and/or edge deployment. An approximation to Bayesian DNN using Monte Carlo Dropout (MC-Dropout) has shown high robustness along with low computational complexity. Enhancing the computational efficiency of the method, we discuss a novel CIM module that can perform in-memory probabilistic dropout in addition to in-memory weight-input scalar product to support the method. We also propose a compute-reuse reformulation of MC-Dropout where each successive instance can utilize the product-sum computations from the previous iteration. Even more, we discuss how the random instances can be optimally ordered to minimize the overall MC-Dropout workload by exploiting combinatorial optimization methods. Application of the proposed CIM-based MC-Dropout execution is discussed for MNIST character recognition and visual odometry (VO) of autonomous drones. The framework reliably gives prediction confidence amidst non-idealities imposed by MC-CIM to a good extent. Proposed MC-CIM with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$16\times 31$ </tex-math></inline-formula> SRAM array, 0.85 V supply, 16nm low-standby power (LSTP) technology consumes 32 pJ for 30 MC-Dropout instances of probabilistic inference in its most optimal computing and peripheral configuration, saving <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\sim 34$ </tex-math></inline-formula> % energy compared to typical execution.
Vision-Aided Path Planning Using Low-Cost Gene Encoding for a Mobile Robot

Path planning is intrinsically regarded as a multi-objective optimization problem (MOOP) that simultaneously optimizes the shortest path and the least collision-free distance to obstacles. This work develops a novel optimized approach using the genetic algorithm (GA) to drive the multi-objective evolutionary algorithm (MOEA) for the path planning of a mobile robot in a given finite environment. To represent the positions of a mobile robot as integer-type genes in a chromosome of the GA, a grid-based method is also introduced to relax the complex environment to a simple grid-based map. The system architecture is composed of a mobile robot, embedded with the robot operating system (ROS), the ArUco system and a laptop, executing the algorithms of path planning and image processing. Both simulations and experimental results are presented to verify the feasibility of the proposed method. In applications, this work can be employed in a commercial ball-collecting or an object-carrying robot.