Reviewerly/exHarmony huggingface dataset:
Supremacy of Quantum Computation in Bioscience: A Technical Review

Beyond traditional computing, quantum computing is based on quantum phenomena like entanglement and superposition. Even though quantum computing is still developing, it has had a tremendous impact on the world of computing by opening up a brand-new dimension. To fully appreciate its potential and advantages, however, there are a number of obstacles to overcome, much like with any other idea or subject. This review explains quantum computing models and identifies some advantages and disadvantages. This systematic review's key contribution is its summary of the state-of-the-art quantum computing models used in numerous fields nowadays. Based on the literature study, it offers new classifications of quantum models and connects the findings to the four main groups of quantum computing models. Evaluation demonstrates that despite their foundation in quantum operations and circuits, the majority of the studied models are either mathematical or algorithmic. In theory, quantum computers are capable of solving some tasks tenfold faster than their classical equivalents. Although the advent of practical quantum computation has not yet occurred, it will have an impact on almost all fields of science. In this analysis, we investigate the potential impact of contemporary quantum algorithms on computational science and informatics.
Arbitrary Programming of Racetrack Resonators Using Low-Loss Phase-Change Material Sb<sub>2</sub>Se<sub>3</sub>

The programmable photonic integrated circuit (PIC) is an enabling technology behind optical interconnects and quantum information processing. Conventionally, the programmability of PICs is driven by the thermo-optic effect, free carrier dispersion, or mechanical tuning. These effects afford either high speed or a large extinction ratio, but all require constant power or bias to maintain the states, which is undesirable for programmability with infrequent switching. Recent progress in programmable PICs based on nonvolatile phase-change materials (PCMs) offers an attractive solution to a truly "set-and-forget" switch that requires zero static energy. Here, we report an essential building block of large-scale programmable PICs─a racetrack resonator with independent control of coupling and phase. We changed the resonance extinction ratio (ER) without perturbing the resonance wavelength, leveraging a programmable unit based on a directional coupler and a low-loss PCM Sb2Se3. The unit is only 33-μm-long and has an operating bandwidth over 50 nm, a low insertion loss (∼0.36 dB), high ER (∼15 dB), and excellent fabrication yield of over 1000 cycles endurance across nine switches. The work is a crucial step toward future large-scale energy-efficient programmable PICs.
The Instrumentation and Future Technology Technical Committee’s Second “Summer School”: Auckland, New Zealand [Technical Committees]

The Instrumentation and Future Technologies Technical Committee’s (IFT-TC) second summer school took place in Auckland, New Zealand, from 30 January to 3 February 2023. The IFT Remote Sensing Summer Schools (IFT-R3S) aim to promote future research in remote sensing; connect Master students, junior Ph.D. students, recent graduates, and young professionals with research groups linked to the IFT-TC; and highlight the educational activities of the IEEE Geoscience and Remote Sensing Society (GRSS). The IFT-R3S focuses on remote sensing instrumentation with aspirations to develop and foster expertise and capacity in this sector. As the first technical activity after the recent formation of a New Zealand GRSS Chapter, a primary objective was to foster connections within New Zealand and the Oceania Region and to heighten New Zealand’s visibility and ambitions in the Earth-observing sector.
CASE STUDIES OF A SENOR-ENHANCED CARE COORDINATION MODEL TO ADDRESS OLDER ADULT HEALTH

Abstract Smart technologies hold enormous potential in the early detection of activity change that may indicate illness or functional decline among older adults. However, little research currently exists on the application of such technologies to community-dwelling older adults. Our program, Age-friendly Sustainable Smart and Equitable Technologies for Aging in Place (ASSETs for AIP) uses open-source smart home technologies and consumer-grade wearable devices to monitor community-dwelling older adults recently discharged from the Show-Me Home program. We employ a novel approach; pairing technology with a care coordination team, comprised of a registered nurse, occupational therapist, and a licensed social worker. This presentation will highlight the function of this team, and how they leverage sensor-generated health information to provide care coordination. Using a clinical interface that displays the sensor-generated health data, this team provides ongoing monitoring of daily activity to identify early changes in routine that may indicate the development of a health problem. As significant changes in health data present, our clinical team works with clients to understand the change and formulate a plan of action. For example, an unexplained spike in heart rate may lead the clinician to discuss recent activity, exertion, and potential modifications in routine for a client. An increase in middle-of-the night bathroom trips may indicate a health change, for the which the client will be coached in what to communicate to their provider. Case studies from our program will be discussed in this presentation, to highlight this model of sensor-enhanced care coordination.
An Improved Hand Sign Recognition using Deep Learning

A population of deaf and mute individuals who are unable to communicate verbally typically utilize hand sign language, but the gap between mute and spoken language is wide. This system's primary aim is to enhance communication between the two groups, hand gestures are different from person to person in terms of representation, shape, and orientation, so there is a problem of linearity which is the main problem to be flexible and arbitrary. Recent systems and software have created various methods and algorithms to solve these problems and build these systems. For hand signal interpretation and motion patterns, algorithms like K-Nearest Neighbor (KNN), Multilevel Super Vector Machine (SVM), and individual experimentation are employed. This study contrasts the efficiency and reliability of CNN, SVM, and KNN algorithms to identify which one delivers the highest accuracy, as well as comprehensively develops software for pattern recognition designed to process this data using real-time hand gestures and database-filtered input. Input English alphabet and point to text output, The efficiency of this model of ours is 93.47%.
ViT-MVT: A Unified Vision Transformer Network for Multiple Vision Tasks

In this work, we seek to learn multiple mainstream vision tasks concurrently using a unified network, which is storage-efficient as numerous networks with task-shared parameters can be implanted into a single consolidated network. Our framework, vision transformer (ViT)-MVT, built on a plain and nonhierarchical ViT, incorporates numerous visual tasks into a modest supernet and optimizes them jointly across various dataset domains. For the design of ViT-MVT, we augment the ViT with a multihead self-attention (MHSE) to offer complementary cues in the channel and spatial dimension, as well as a local perception unit (LPU) and locality feed-forward network (locality FFN) for information exchange in the local region, thus endowing ViT-MVT with the ability to effectively optimize multiple tasks. Besides, we construct a search space comprising potential architectures with a broad spectrum of model sizes to offer various optimum candidates for diverse tasks. After that, we design a layer-adaptive sharing technique that automatically determines whether each layer of the transformer block is shared or not for all tasks, enabling ViT-MVT to obtain task-shared parameters for a reduction of storage and task-specific parameters to learn task-related features such that boosting performance. Finally, we introduce a joint-task evolutionary search algorithm to discover an optimal backbone for all tasks under total model size constraint, which challenges the conventional wisdom that visual tasks are typically supplied with backbone networks developed for image classification. Extensive experiments reveal that ViT-MVT delivers exceptional performances for multiple visual tasks over state-of-the-art methods while necessitating considerably fewer total storage costs. We further demonstrate that once ViT-MVT has been trained, ViT-MVT is capable of incremental learning when generalized to new tasks while retaining identical performances for trained tasks. The code is available at https://github.com/XT-1997/vitmvt.
Improving Uncertainty Quantification of Variance Networks by Tree-Structured Learning

To improve the uncertainty quantification of variance networks, we propose a novel tree-structured local neural network model that partitions the feature space into multiple regions based on uncertainty heterogeneity. A tree is built upon giving the training data, whose leaf nodes represent different regions where region-specific neural networks are trained to predict both the mean and the variance for quantifying uncertainty. The proposed uncertainty-splitting neural regression tree (USNRT) employs novel splitting criteria. At each node, a neural network is trained on the full data first, and a statistical test for the residuals is conducted to find the best split, corresponding to the two subregions with the most significant uncertainty heterogeneity between them. USNRT is computationally friendly, because very few leaf nodes are sufficient and pruning is unnecessary. Furthermore, an ensemble version can be easily constructed to estimate the total uncertainty, including the aleatory and epistemic. On extensive UCI datasets, USNRT or its ensemble shows superior performance compared to some recent popular methods for quantifying uncertainty with variances. Through comprehensive visualization and analysis, we uncover how USNRT works and show its merits, revealing that uncertainty heterogeneity does exist in many datasets and can be learned by USNRT.
EXPLORING LINKS BETWEEN COGNITIVE AGING AND FEATURES OF WORKING MEMORY VIA COMPUTATIONAL MODELING

Abstract With an aging population, understanding cognitive aging is essential for developing interventions to support independence and well-being. Additionally, there is a need for sensitive measures that can differentiate subtle cognitive decline related to neurodegenerative disease from age-related changes. In particular, Alzheimer’s Disease and Related Dementia (ADRD) increases in prevalence with age and involves progressive cognitive decline. The Color Shapes task (CST) is a visual working memory task that has been shown to be sensitive to early indicators for ADRD. CST involves feature binding (e.g., mental representation of features into a unified whole) that is impaired in people with ADRD. In an experiment, we manipulated different CST properties to study whether drift diffusion modeling (DDM) can account for individual differences in latent decision-making processes generating manifest CST performance scores. DDM allows us to quantify theoretically meaningful cognitive parameters that underlie task performance captured in reaction times and accuracies. 68 U.S. community-residing adults (28-80 years) completed daily smartphone assessments across 8 days with experimentally manipulated versions of the CST. We implemented a single-step Bayesian estimation of experimental conditions contrasts, latent DDM parameters, and their associations. We show how individual differences in experimental contrasts were related to age in terms of cognitive (drift rate), non-cognitive (non-decision time), and meta-cognitive (boundary separation) parameters. The results highlight the benefits of using cognitive process models with optimized cognitive tasks to delineate features of cognitive aging for ADRD research.
ATTACKED BY BOTS AND HACKED BY BAD APPLES: LESSONS LEARNED NAVIGATING RESEARCH PARTICIPANT FRAUD

Abstract In a time of emerging technologies for participant recruitment and data collection, researchers need proven and comprehensive strategies to protect research integrity. Incorporation of proactive, rather than reactive, methods to prevent fraudulence will mitigate additional stressors and headaches associated with fraud and fraud detection. This session presents a case which describes a researcher’s experience detecting, navigating, problem-solving, and then managing participant fraud that occurred twice in a multi-methods study. Participants were recruited from a senior center and two YMCA sites using social media platforms and email distribution lists which linked them with an online survey. After the initial study launch, a “bot attack” with “bot fast” deception within the study survey caused the study to shut down. The researcher worked with the study team, community sites, and IRB to detect potential causes of fraud and implement protective factors to ensure quality control and deter the bot system. Unfortunately, after the second study launch, the survey was once again successfully attacked. This time the researcher chose to work with the study team in order to create a step-wise rigorous approach to verifying accurate data and participants, while working to maintain trust of the community sites and real participants. Finally, to mitigate further fraud, the researcher pivoted to the use of ResearchMatch to ensure recruitment goals and trustworthy data. Within this presentation, the researcher will share lessons learned in survey fraud, proactive strategies to prevent fraudulence, communication methods interacting with IRB, study sites, and participants, and maneuvering perils involved in manuscript preparation.
DIVIS

The Digital Interactive Victim Intake Simulator ("DIVIS") is an interactive, agent-based simulated training tool that has been deployed at the U.S. Army's Sexual Harassment/Assault Response Prevention Program ("SHARP") Academy since May 2021. The system allows student Sexual Assault Response Coordinators ("SARCs") and Victim Advocates ("VAs") to practice critical interpersonal intake skills needed when conducting the initial interview of a survivor of military sexual assault. Currently the system includes two scenarios -- one with a male victim and a second with a female victim -- with two more scenarios under development. Each victim exhibits one of a possible three different emotional vectors, (e.g., angry, ashamed or defensive). Scenarios can run multiple times, giving trainees the ability to navigate through various potential story paths based on how they engage with the victim during each session.
Innovations in Robotics Education: Augmented Reality

Augmented Reality (AR) is a technological tool that provides interactive experiences to the user by overlapping virtual objects in a real environment. This paper presents a tool developed using augmented reality to study and teach robotic manipulators, focused on universities without the economic resources to acquire a serial manipulator. Three programs were developed that allow the student to visualize and rectify essential concepts of robotics such as the transformation of coordinate systems, Denavit - Hartenberg methodology, and configuration of a robotic manipulator, among others. The application allows the student to interact virtually with different manipulators, control the robot’s joints using the keyboard, and visualize real-time distances and parameters concerning a fixed coordinate system on the screen. Considering that the manipulators are rendered, it is possible to perform path planning and collision analysis on a path given by the user. The application is easily modifiable, including new manipulators and developing didactic strategies for students.
EIV-BT-ABE: Efficient Attribute-Based Encryption With Black-Box Traceability Based On Encrypted Identity Vector

The fine-grain of ciphertext-policy attribute-based encryption (CP-ABE) offers advantages through the amalgamation of key and user attributes; however, it also brings the issue of key misuse. To circumvent the tracking mechanisms of the white-box algorithm, malicious users manipulate the decryption key and encryption algorithm, encapsulating them to create a black-box decryption device. This necessitates black-box traceability for supervision purposes. In this article, we employ n-bit encrypted binary vectors to depict the user’s identity and subsequently convert it into partial decryption privileges. When encrypting plaintext, data owners can utilize a “vague specification" to define the identity vector of qualified decryptors. Furthermore, based on the “vague specification" mechanism, we have devised a pioneering active black-box tracing algorithm. Integrating this algorithm with CP-ABE, we propose the black-box traceable CP-ABE (EIV-BT-ABE) scheme. Our EIV-BT-ABE scheme attains strong traceability with low time complexity, effectively reducing decryption and encryption time costs. The experiment substantiated the efficiency of our scheme while demonstrating its adherence to IND-CPA security.
Probabilistic Modeling of Image Aesthetic Assessment towards Measuring Subjectivity

Assessing image aesthetics is a challenging computer vision task. One reason is that aesthetic preference is highly subjective and may vary significantly among people for certain images. Thus, it is important to properly model and quantify such <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">subjectivity</i> , but there has not been much effort to resolve this issue. In this paper, we propose a novel probabilistic framework that can model and quantify subjective aesthetic preference based on the subjective logic. In this framework, the rating distribution is modeled as a beta distribution, from which the probabilities of being definitely pleasing, being definitely unpleasing, and being uncertain can be obtained. We use the probability of being uncertain to define an intuitive metric of subjectivity. Furthermore, we present a method to learn deep neural networks for prediction of image aesthetics, which is shown to be effective in improving the performance of subjectivity prediction via experiments.
A Blockchain-based Hierarchical Trust Management Scheme for IoT

The diverse and resource-constrained nature of Internet of Things (IoT) nodes presents challenges in terms of storage, computing capacity, scalability, and privacy. Effective trust management is crucial for gathering reliable data, enhancing privacy, and detecting and mitigating malicious nodes within the IoT ecosystem. However, traditional trust management mechanisms are vulnerable to security attacks, such as central node failures, and often neglect the unique constraints of IoT nodes. This paper introduces a blockchain-based hierarchical trust management framework aimed at addressing the limitations of conventional trust management systems for IoT. The proposed framework evaluates node reputation and organizational trust across two levels: intra-organizational and inter-organizational. At the intra-organizational level, a lightweight blockchain calculates trust and records node reputations. Upon reaching the local blockchain's length threshold, node reputation values are aggregated and inscribed in the global blockchain. In contrast, the global blockchain calculates and records organizational trust. Inter-organizational trust is assessed through direct and indirect trust methodologies. The proposed approach is assessed via a confusion matrix, demonstrating its efficacy in identifying and eliminating low-resource malicious nodes with minimal computational overhead.
Educational robotics to enhance knowledge and skills in higher education: A systematic review

Because of its ability to prepare students for the 21st century world of work, educational robotics (RE) has assumed a prominent role in education. Following the methodology provided by Kitchenham, we conducted a systematic literature review (SLR) to provide a comprehensive overview of studies, research, and practices related to robotics education in higher education. The study focuses on identifying the most commonly used RE hardware-based platforms in academic research publications, the type of knowledge intended to be imparted, and the skills or competencies reinforced by the platforms. Within the findings we can underline that there is no exclusive platform, but rather commercial ones are used, made by the researchers themselves, and even adapted from robots that were not designed for instructional purposes. Moreover, the subjects intended to be taught were all related to Science, Technology, Engineering and Mathematics (STEM) and oriented to electrical engineering, electronics, robotics, and computer science professions. On the other hand, the skills that improved the most were computational thinking, cooperation and sophisticated problem solving, which are essential for 21st century job descriptions.
Heterogeneity-aware Load Balancing in Serverless Computing Environments

The current trend in cloud computing is the growing adoption of microservices. Microservices are specialized components of an application that can be deployed and scaled individually, potentially replacing monolithic applications. To meet this demand, cloud providers offer function-as-a-service (FaaS) or serverless computing. Additionally, there is an increasing use of containerized environments in Internet of Things (IoT) applications. It has been shown that implementing serverless computing at the edge of an IoT network for executing tasks can reduce the overall execution time of these tasks. In cloud and edge computing environments, the infrastructure typically exhibits heterogeneity, and this heterogeneity can significantly impact the performance of different invocations of a FaaS function. This paper addresses infrastructure heterogeneity by partitioning a heterogeneous cluster into homogeneous pools based on similar resource characteristics and profiling function performance. The key contributions of this paper are: (1) a serverless architecture design that extends the function placement and load-balancing capabilities of current serverless platforms to effectively manage infrastructure heterogeneity, (2) a load-balancing approach that efficiently utilizes multi-core hardware, and (3) an implementation of the architecture, thereby evaluating the impact of the extended capabilities.
A Network Survivability Evaluation Method Based on PDRR Model

Due to few researches on network survivability evaluation at present, this paper proposed an index system of network survivability evaluation based on the PDRR strategy. In view of the discreteness caused by the strong subjectivity of index assignment, the improved TOPSIS model is introduced for modeling calculation. According to the preset rules, the virtual Marine network survivability evaluation is carried out with the evaluation index quantification method. The calculation results show that the improved TOPSIS method can effectively suppress the discreteness of subjective evaluation by experts, which made the selected index system reasonable and provided a basis for the evaluation of the network's survivability.
AI algorithms used for fault diagnosis of operating PV modules with thermal images

The present study has as its purpose the explanation of the evaluation of an AI application that allows the diagnosis of failures in PV modules in collection by means of thermographic images. That is, currently renewable energy systems represent a field of significant importance within Society, for this reason, research and studies are required to deploy more clearly the implementation of improvements and technological trends to contribute to society. The following article cites many authors who have played a significant role in the study of PV modules and who have also implemented systemic or software improvements to contribute with research to guide the innovation of these renewable energy sources.