Reviewerly/exHarmony huggingface dataset:
Small Sampling Overhead Error Mitigation for Quantum Circuits

Probabilistic error cancellation (PEC) is a promising error mitigation technique that reduces the error rate without auxiliary qubits. However, PEC has two problems that need to be resolved: (1) There is no good PEC technique for parameterized gates. (2) Sampling overhead grows exponentially with the number of PEC mitigated gates. We first propose a parameterized gate PEC (PGPEC) that mitigates the error without fully characterizing the gates, as the original PEC requires. The result shows that the number of gates requiring characterization for a thousand random circuits can be reduced by 97% or more. We next propose two novel approaches to solving the second problem. We propose a macro gate PEC (MGPEC) technique that aggregates multiple gates as a single macro gate to reduce the exponent of the sampling overhead. MGPEC reduces the sampling overhead by 49% on the QFT7 under the IBMQ noise model, which simulates real operation conditions of quantum circuits. We propose a design diversity PEC (DDPEC) technique to reduce the exponential basis of the sampling overhead. The results show that our DDPEC with design diversity check reduces overall sampling overhead by 13% on the QFT7 circuit under the IBM Q noise model. Combining the DDPEC with the MGPEC, we can reduce overall sampling overhead by 73%.
Topology optimization of natural frequency in rotating-mirror structure for the ultra-high-speed camera

Polyhedron rotating scanning mirror (PRM) has very important applications in the fields of ultra-high-speed cameras, ultrashort pulse micro/nano machining, laser scanning, and Q-switching. The natural frequency of PRM is crucial for PRM design. In this paper, the application of a topology-optimization technique to improve the fundamental natural frequency of PRM for ultra-high-speed cameras was proposed based on the solid isotropic material with the penalization method and the method of moving asymptotes to investigate the structural optimization design. Moreover, the internal structure of the PRM was optimized based on the pseudo-density distribution. In the optimized structure, the three sharp corners with smaller pseudo-density material were removed and replaced by three irregular holes and three circular apertures with the same radius. Under identical conditions, the first natural frequency of the optimization structure was promoted from 4, 746.3 Hz to 5, 366.1 Hz, increased by 13.1%, which represents an obvious improvement in the image quality obtained by using ultra-high-speed cameras. The topology-optimization model and corresponding methodology reported herein provide a perspective and application area to effectively improve the fundamental frequency of a PRM without changing the external structure of this component
Distributed Representation for Assembly Code

In recent years, the number of similar software products with many common parts has been increasing due to the reuse and plagiarism of source code in the software development process. Pattern matching, which is an existing method for detecting similarity, cannot detect the similarities between these software products and other programs. It is necessary, for example, to detect similarities based on commonalities in both functionality and control structures. At the same time, detailed software analysis requires manual reverse engineering. Therefore, technologies that automatically identify similarities among the arge amounts of code present in software products in advance can reduce these oads. In this paper, we propose a representation earning model to extract feature expressions from assembly code obtained by statically analyzing such code to determine the similarity between software products. We use assembly code to eliminate the dependence on the existence of source code or differences in development anguage. The proposed approach makes use of Asm2Vec, an existing method, that is capable of generating a vector representation that captures the semantics of assembly code. The proposed method also incorporates information on the program control structure. The control structure can be represented by graph data. Thus, we use graph embedding, a graph vector representation method, to generate a representation vector that reflects both the semantics and the control structure of the assembly code. In our experiments, we generated expression vectors from multiple programs and used clustering to verify the accuracy of the approach in classifying similar programs into the same cluster. The proposed method outperforms existing methods that only consider semantics in both accuracy and execution time.
WorldPoint: Finger Pointing as a Rapid and Natural Trigger for In-the-Wild Mobile Interactions

Pointing with one's finger is a natural and rapid way to denote an area or object of interest. It is routinely used in human-human interaction to increase both the speed and accuracy of communication, but it is rarely utilized in human-computer interactions. In this work, we use the recent inclusion of wide-angle, rear-facing smartphone cameras, along with hardware-accelerated machine learning, to enable real-time, infrastructure-free, finger-pointing interactions on today's mobile phones. We envision users raising their hands to point in front of their phones as a "wake gesture". This can then be coupled with a voice command to trigger advanced functionality. For example, while composing an email, a user can point at a document on a table and say "attach". Our interaction technique requires no navigation away from the current app and is both faster and more privacy-preserving than the current method of taking a photo.
Interactive 3D Annotation of Objects in Moving Videos from Sparse Multi-view Frames

Segmenting and determining the 3D bounding boxes of objects of interest in RGB videos is an important task for a variety of applications such as augmented reality, navigation, and robotics. Supervised machine learning techniques are commonly used for this, but they need training datasets: sets of images with associated 3D bounding boxes manually defined by human annotators using a labelling tool. However, precisely placing 3D bounding boxes can be difficult using conventional 3D manipulation tools on a 2D interface. To alleviate that burden, we propose a novel technique with which 3D bounding boxes can be created by simply drawing 2D bounding rectangles on multiple frames of a video sequence showing the object from different angles. The method uses reconstructed dense 3D point clouds from the video and computes tightly fitting 3D bounding boxes of desired objects selected by back-projecting the 2D rectangles. We show concrete application scenarios of our interface, including training dataset creation and editing 3D spaces and videos. An evaluation comparing our technique with a conventional 3D annotation tool shows that our method results in higher accuracy. We also confirm that the bounding boxes created with our interface have a lower variance, likely yielding more consistent labels and datasets.
Cross-Domain Gesture Sequence Recognition for Two-Player Exergames using COTS mmWave Radar

Wireless-based gesture recognition provides an effective input method for exergames. However, previous works in wireless-based gesture recognition systems mainly recognize one primary user's gestures. In the multi-player scenario, the mutual interference between users makes it difficult to predict multiple players' gestures individually. To address this challenge, we propose a flexible FMCW-radar-based system, RFDual, which enables real-time cross-domain gesture sequence recognition for two players. To eliminate the mutual interference between users, we extract a new feature type, biased range-velocity spectrum (BRVS), which only depends on a target user. We then propose customized preprocessing methods (cropping and stationary component removal) to produce environment-independent and position-independent inputs. To enhance RFDual's resistance to unseen users and articulating speeds, we design effective data augmentation methods, sequence concatenating, and randomizing. RFDual is evaluated with a dataset containing only unseen gesture sequences and achieves a gesture error rate of 1.41%. Extensive experimental results show the impressive robustness of RFDual for data in new domains, including new users, articulating speeds, positions, and environments. These results demonstrate the great potential of RFDual in practical applications like two-player exergames and gesture/activity recognition for drivers and passengers in the cab.
BlendMR: A Computational Method to Create Ambient Mixed Reality Interfaces

Mixed Reality (MR) systems display content freely in space, and present nearly arbitrary amounts of information, enabling ubiquitous access to digital information. This approach, however, introduces clutter and distraction if too much virtual content is shown. We present BlendMR, an optimization-based MR system that blends virtual content onto the physical objects in users’ environments to serve as ambient information displays. Our approach takes existing 2D applications and meshes of physical objects as input. It analyses the geometry of the physical objects and identifies regions that are suitable hosts for virtual elements. Using a novel integer programming formulation, our approach then optimally maps selected contents of the 2D applications onto the object, optimizing for factors such as importance and hierarchy of information, viewing angle, and geometric distortion. We evaluate BlendMR by comparing it to a 2D window baseline. Study results show that BlendMR decreases clutter and distraction, and is preferred by users. We demonstrate the applicability of BlendMR in a series of results and usage scenarios.
Automation of Explainability Auditing for Image Recognition

XAI requires artificial intelligence systems to provide explanations for their decisions and actions for review. Nevertheless, for big data systems where decisions are made frequently, it is technically impossible to have an expert monitor every decision. To solve this problem, the authors propose an explainability auditing method for image recognition whether the explanations are relevant for the decision made by a black box model, and involve an expert as needed when explanations are doubtful. The explainability auditing system classifies explanations as weak or satisfactory using a local explainability model by analyzing the image segments that impacted the decision. This version of the proposed method uses LIME to generate the local explanations as superpixels. Then a bag of image patches is extracted from the superpixels to determine their texture and evaluate the local explanations. Using a rooftop image dataset, the authors show that 95.7% of the cases to be audited can be detected by the proposed method.
Introduction to Application Development Best Practices

Here we are at the heart of the matter! As you might expect, we will take your time developing an API in this book. Before developing an API with ASP.NET Core 8, in this chapter, we will review the basics of any software development, whether it is an API or any other type of software, such as a mobile application or a desktop application. Ideally, any application should respect the following development fundamentals: programming basics, structuring code in a clean architecture, writing readable, maintainable (and easy to debug!) code, and keeping your application safe with the Open Worldwide Application Security Project (OWASP) principles.
UbiSurface: A Robotic Touch Surface for Supporting Mid-air Planar Interactions in Room-Scale VR

Room-scale VR has been considered an alternative to physical office workspaces. For office activities, users frequently require planar input methods, such as typing or handwriting, to quickly record annotations to virtual content. However, current off-the-shelf VR HMD setups rely on mid-air interactions, which can cause arm fatigue and decrease input accuracy. To address this issue, we propose UbiSurface, a robotic touch surface that can automatically reposition itself to physically present a virtual planar input surface (VR whiteboard, VR canvas, etc.) to users and to permit them to achieve accurate and fatigue-less input while walking around a virtual room. We design and implement a prototype of UbiSurface that can dynamically change a canvas-sized touch surface's position, height, and pitch and yaw angles to adapt to virtual surfaces spatially arranged at various locations and angles around a virtual room. We then conduct studies to validate its technical performance and examine how UbiSurface facilitates the user's primary mid-air planar interactions, such as painting and writing in a room-scale VR setup. Our results indicate that this system reduces arm fatigue and increases input accuracy, especially for writing tasks. We then discuss the potential benefits and challenges of robotic touch devices for future room-scale VR setups.
Using Online Videos as the Basis for Developing Design Guidelines: A Case Study of AR-Based Assembly Instructions

Design guidelines serve as an important conceptual tool to guide designers of interactive applications with well-established principles and heuristics. Consulting domain experts is a common way to develop guidelines. However, experts are often not easily accessible, and their time can be expensive. This problem poses challenges in developing comprehensive and practical guidelines. We propose a new guideline development method that uses online public videos as the basis for capturing diverse patterns of design goals and interaction primitives. In a case study focusing on AR-based assembly instructions, we apply our novel Identify-Rationalize pipeline, which distills design patterns from videos featuring AR-based assembly instructions (N=146) into a set of guidelines that cover a wide range of design considerations. The evaluation conducted with 16 AR designers indicated that the pipeline is useful for generating comprehensive guidelines. We conclude by discussing the transferability and practicality of our method.
Approximate and ensemble local entanglement transformations for multipartite states

Understanding multipartite entanglement is a key goal in quantum information. Entanglement in pure states can be characterized by considering transformations under local operations assisted by classical communication (LOCC). However, it has been shown that, for $n\ensuremath{\ge}5$ parties, multipartite pure states are generically isolated, i.e., they can be neither reached nor transformed under LOCC. Nonetheless, in any real laboratory, one never deterministically transforms a pure initial state exactly to a pure target state. Instead, one transforms a mixed state near the initial state to an ensemble that is on average close to the target state. This motivates studying approximate LOCC transformations. After reviewing in detail the known results in the bipartite case, we present the gaps that remain open in the multipartite case. While the analysis of the multipartite setting is much more technically involved due to the existence of different stochastic LOCC (SLOCC) classes, certain features simplify in the approximate setting. In particular, we show that it is sufficient to consider pure initial states, that it is sufficient to consider LOCC protocols with finitely many rounds of communication, and that approximate transformations can be approximated by ensemble transformations within an SLOCC class. Then we formally define a hierarchy of different forms of approximate transformations that are relevant from a physical point of view. Whereas this hierarchy collapses in the bipartite case, we show that this is not the case for the multipartite setting, which is fundamentally richer. To wit, we show that optimal multipartite approximate transformations are not generally deterministic, that ensemble transformations within an SLOCC class can achieve a higher fidelity than deterministic transformations within an SLOCC class, and that there are approximate transformations with no deterministic transformations nearby.
New Business Models for Society 5.0

In the evolving landscape of technological advancements, the Internet of Behavior (IoB) emerges as a pivotal concept, harnessing the power of data to understand and influence humans. IoB finds its most profound application in Society 5.0, a vision of a super-smart society where digital and physical realms harmoniously coexist. New business and economic models like the circular economy, sharing economy, smart cities, platform business model, social enterprise model, and blockchain technology will be explored. Emerging healthcare, agriculture, hospitality, and energy business models are discussed. Challenges arise as innovative organizations emerge to create a more sustainable and inclusive culture. Data privacy and security, blockchain high energy consumption, technologically unprepared workforce, and artificial intelligence bias must be addressed. This chapter aims to demonstrate emerging economies, business models, and related technologies that facilitate Society 5.0 as it evolves from human behavior insights gained from IoB.
Applications of Missing Data Imputation Methods in Wastewater Treatment Plants

Missing data pose a big challenge in the field of wastewater treatment, representing a frequent issue in data quality that can result in misleading analyses and compromised decision-making accuracy. The initial step in data preprocessing involves the estimation and handling of missing values. The primary aim to conduct a comprehensive examination of the existing research concerning missing value imputation in wastewater treatment plants (WWTPs). The focus is specifically on identifying and outlining various imputation techniques employed in this field, while paying close attention to their respective strengths and limitations. To ensure a methodical approach, this study adopts the systematic literature review (SLR) using Kitchenham’s guidelines. In order to gather relevant and up-to-date papers, the research leverages the scientific database "Scopus" to retrieve and analyze all pertinent papers during the search process. By doing so, this research aims to contribute valuable insights into the different strategies used for imputing missing values in WWTPs and to shed light on their practical implications and potential drawbacks. Form 599, a total of 16 research papers were selected to assess the review questions. Finally, several recommendations were given to address the limitations identified in the reviewed studies and to contribute to more accurate and reliable data analysis and decision-making in the wastewater treatment domain.
A Multitask Learning System for Trait-based Automated Short Answer Scoring

Evaluating students' responses and providing feedback in the education system is widely acknowledged. However, while most research on Automated Essay Scoring (AES) has focused on generating a final score for given responses, only a few studies have attempted to generate feedback. These studies often rely on statistical features and fail to capture coherence and content-based features. To address this gap, we proposed a multitask learning system that can capture linguistic, coherence, and content-based features with Bidirectional Encoder Representations from Transformers (BERT) sentence by sentence and generate overall essay and trait scores. Our proposed system outperformed other existing models, achieving Quadratic Weighted Kappa (QWK) scores of 0.766, 0.69, and 0.701 compared to human rater scores. We evaluated our model on the Automated Student Assessment Prize (ASAP) Kaggle and operating system (OS) data set. When compared with other prescribed models proposed to multitask learning system is a promising step towards more effective and comprehensive writing assessment and feedback.
Code-Mixed Sentiment Analysis using Transformer for Twitter Social Media Data

The underrepresentation of the Indonesian language in the field of Natural Language Processing (NLP) can be attributed to several key factors, including the absence of annotated datasets, limited language resources, and a lack of standardization in these resources. One notable linguistic phenomenon in Indonesia is code-mixing between Bahasa Indonesia and English, which is influenced by various sociolinguistic factors, including individual speaker characteristics, the linguistic environment, the societal status of languages, and everyday language usage. In an effort to address the challenges posed by code-mixed data, this research project has successfully created a code-mixed dataset for sentiment analysis. This dataset was constructed based on keywords derived from the sociolinguistic phenomenon observed among teenagers in South Jakarta. Utilizing this newly developed dataset, we conducted a series of experiments employing different pre-processing techniques and pre-trained models. The results of these experiments have demonstrated that the IndoBERTweet pre-trained model is highly effective in solving sentiment analysis tasks when applied to Indonesian-English code-mixed data. These experiments yielded an average precision of 76.07%, a recall of 75.52%, an F-1 score of 75.51%, and an accuracy of 76.56%.
Balancing Granularity and Context in Designing Evidence Models for Stealth Assessment

In the context of game-based learning environments, stealth assessment unobtrusively measures learning outcomes. However, designing a robust evidence model requires careful consideration of learning behaviors in context. This chapter presents a case on designing evidence models for stealth assessment in a game-based learning environment. In this study, the authors employ a finite mixture model to analyze players' pause behaviors in the game, interpreting the meanings of these behaviors under different contexts. The findings contribute to the understanding of how to balance granularity and context in designing evidence models, proving valuable for enhancing the efficacy of stealth assessments for game-based learning.
Detection of temporal fluctuation in superconducting qubits for quantum error mitigation

We have investigated instability of a superconducting quantum computer by continuously monitoring the qubit output. We found that qubits exhibit a step-like change in the error rates. This change is repeatedly observed, and each step persists for several minutes. By analyzing the correlation between the increased errors and anomalous variance of the output, we demonstrate quantum error mitigation based on post-selection. We have reduced the errors from 5.4% to 1.6% in a Bell state measurement and from 17.5% to 12.0% in a quantum volume circuit. Numerical analysis on the proposed method was also conducted.