Reviewerly/exHarmony huggingface dataset:
MAQA: a quantum framework for supervised learning

Quantum Machine Learning has the potential to improve traditional machine learning methods and overcome some of the main limitations imposed by the classical computing paradigm. However, the practical advantages of using quantum resources to solve pattern recognition tasks are still to be demonstrated. This work proposes a universal, efficient framework that can reproduce the output of a plethora of classical supervised machine learning algorithms exploiting quantum computation's advantages. The proposed framework is named Multiple Aggregator Quantum Algorithm (MAQA) due to its capability to combine multiple and diverse functions to solve typical supervised learning problems. In its general formulation, MAQA can be potentially adopted as the quantum counterpart of all those models falling into the scheme of aggregation of multiple functions, such as ensemble algorithms and neural networks. From a computational point of view, the proposed framework allows generating an exponentially large number of different transformations of the input at the cost of increasing the depth of the corresponding quantum circuit linearly. Thus, MAQA produces a model with substantial descriptive power to broaden the horizon of possible applications of quantum machine learning with a computational advantage over classical methods. As a second meaningful addition, we discuss the adoption of the proposed framework as hybrid quantum-classical and fault-tolerant quantum algorithm.
Depth Invariant 3D-CU-Net Model with Completely Connected Dense Skip Networks for MRI Kidney Tumor Segmentation

Due to the impact and importance of the kidney objects in human body, the kidney tumor analysis from three dimensional CT and MRI medical images becomes a pivotal research topic, which helps in diagnosing the kidney diseases like kidney stones, polycystic and kidney tumors etc.In deep learning, U-Net became a prominent and reliable solution for kidney image analysis and objects segmentation process.Although several research works were focused on kidney object detection and tumor segmentation from medical images, they are suffering from some intrinsic limitations due to: variance in network depths, enforced feature fusion, segmentation errors and inaccuracy.In order to address these limitations in kidney tumor segmentation process, in this paper we proposed the 3D-CU-Net model for kidney tumor segmentation, which is a custom variant of the U-Net.In 3D-CU-Net, the encoder-decoder network model is unified to tolerate the depth invariance issues, while training various input images with the same model.Completely connected dense skip connections are designed at each layer of 3D-CU-Net, to control the enforced feature fusion and to extract the crucial features.An integrated loss function is designed with Binary Cross Entropy (BCE) and Soft-Dice Coefficient (SDC) to mitigate the segmentation errors and inaccuracy.Experiments on TCGA-KIRC dataset with 3D-CU-NET recorded the high accuracy in kidney tumor segmentation with mIoU (91.21%) and mDSC (92.69%).
OCCAM-v2: Combining Static and Dynamic Analysis for Effective and Efficient Whole-Program Specialization

Leveraging scalable pointer analysis, value analysis, and dynamic analysis.
An easier-to-align Hong–Ou–Mandel interference demonstration

The Hong-Ou-Mandel interference experiment is a fundamental demonstration of nonclassical interference and a basis for many investigations of quantum information. This experiment involves the interference of two photons reaching a symmetric beamsplitter. When the photons are made indistinguishable in all possible ways, an interference of quantum amplitudes results in both photons always leaving the same beamsplitter output port. Thus, a scan of distinguishable parameters, such as the arrival time difference of the photons reaching the beamsplitter, produces a dip in the coincidences measured at the outputs of the beamsplitter. The main challenge for its implementation as an undergraduate laboratory is the alignment of the photon paths at the beamsplitter. We overcome this difficulty by using a pre-aligned commercial fiber-coupled beamsplitter. In addition, we use waveplates to vary the distinguishability of the photons by their state of polarization. We present a theoretical description at the introductory quantum mechanics level of the two types of experiments, plus a discussion of the apparatus alignment and list of parts needed.
Scheduling IDK classifiers with arbitrary dependences to minimize the expected time to successful classification

Abstract This paper introduces and evaluates a general construct for trading off accuracy and overall execution duration in classification-based machine perception problems—namely, the generalized IDK classifier cascade . The aim is to select the optimal sequence of classifiers required to minimize the expected (i.e. average) execution duration needed to achieve successful classification, subject to a constraint on quality, and optionally a latency constraint on the worst-case execution duration. An IDK classifier is a software component that attempts to categorize each input provided to it into one of a fixed set of classes, returning “I Don’t Know” (IDK) if it is unable to do so with the required level of confidence. An ensemble of several different IDK classifiers may be available for the same classification problem, offering different trade-offs between effectiveness (i.e. the probability of successful classification) and timeliness (i.e. execution duration). A model for representing such characteristics is defined, and a method is proposed for determining the values of the model parameters for a given ensemble of IDK classifiers. Optimal algorithms are developed for sequentially ordering IDK classifiers into an IDK cascade, such that the expected duration to successfully classify an input is minimized, optionally subject to a latency constraint on the worst-case overall execution duration of the IDK cascade. The entire methodology is applied to two real-world case studies. In contrast to prior work, the methodology developed in this paper caters for arbitrary dependences between the probabilities of successful classification for different IDK classifiers. Effective practical solutions are developed considering both single and multiple processors.
Efficient Sampling-based for Mobile Robot Path Planning in a Dynamic Environment Based on the Rapidly-exploring Random Tree and a Rule-template Sets

This study presents an efficient path planning method for mobile robots in a dynamic environment. The method is based on the rapidly-exploring random tree (RRT) algorithm. The two primary processes in mobile robot path planning in a dynamic environment are initial path planning and path re-planning. In order to generate a feasible initial path with fast convergence speed, we used a hybridization of rapidly-exploring random tree star and ant colony systems (RRT-ACS). When an obstacle obstructs the initial path, the path re-planner must be executed. In addition to the RRT-ACS algorithm, we proposed using a rule-template set based on the mobile robot in dynamic environment scenes during the path re-planner process. This novel algorithm is called RRT-ACS with Rule-Template Sets (RRT-ACS+RT). We conducted many benchmark simulations to validate the proposed method in a real dynamic environment. The performance of the proposed method is compared to the state-of-the-art path planning algorithms: RRT*FND and MOD-RRT*. Numerous experimental results demonstrate that the proposed method outperforms other comparison algorithms. The results show that the proposed method is suitable for the use on robots that need to navigate in a dynamic environment, such as self-driving cars.
BGR Images-Based Human Fall Detection Using ResNet-50 and LSTM

A healthy food intake helps people to live a healthy life. In the present era, due to heavy workload or having less time, people are more dependent on junk food or either on skipping food. This may cause different types of diseases like low/high blood pressure, diabetic etc. Human fall is one of the effects of these diseases. The early prediction of human fall detection is to cure people and reduce morbidity and mortality. Various fall detection systems exist, but they have certain limitations such as limited flexibility, single purpose, difficulties in processing vast data, and strong unpredictability. To address these challenges, this proposed model combines ResNet-50 architecture with the OpenCV library, which is primarily responsible for extracting features from BGR images. The lambda layer aids in reducing the final dimension, and the dense layer comes after ResNet-50. The proposed fall detection system detects falls with accuracy of 96% and a sensitivity of 100% on the UR fall dataset, which is publicly accessible records on activities of daily living (ADL) and fall videos.
Living Memories: AI-Generated Characters as Digital Mementos

Every human culture has developed practices and rituals associated with remembering people of the past - be it for mourning, cultural preservation, or learning about historical events. In this paper, we present the concept of "Living Memories": interactive digital mementos that are created from journals, letters and data that an individual have left behind. Like an interactive photograph, living memories can be talked to and asked questions, making accessing the knowledge, attitudes and past experiences of a person easily accessible. To demonstrate our concept, we created an AI-based system for generating living memories from any data source and implemented living memories of the three historical figures "Leonardo Da Vinci", "Murasaki Shikibu", and "Captain Robert Scott". As a second key contribution, we present a novel metrics scheme for evaluating the accuracy of living memory architectures and show the accuracy of our pipeline to improve over baselines. Finally, we compare the user experience and learning effects of interacting with the living memory of Leonardo Da Vinci to reading his journal. Our results show that interacting with the living memory, in addition to simply reading a journal, increases learning effectiveness and motivation to learn about the character.
A Stress Test for Robustness of Photo Response Nonuniformity (Camera Sensor Fingerprint) Identification on Smartphones

In the field of forensic imaging, it is important to be able to extract a camera fingerprint from one or a small set of images known to have been taken by the same camera (or image sensor). Note that we are using the word fingerprint because it is a piece of information extracted from images that can be used to identify an individual source camera. This technique is very important for certain security and digital forensic situations. Camera fingerprint is based on a certain kind of random noise present in all image sensors that is due to manufacturing imperfections and is, thus, unique and impossible to avoid. Photo response nonuniformity (PRNU) has become the most widely used method for source camera identification (SCI). In this paper, a set of attacks is designed and applied to a PRNU-based SCI system, and the success of each method is systematically assessed both in the case of still images and in the case of video. An attack method is defined as any processing that minimally alters image quality and is designed to fool PRNU detectors or, in general, any camera fingerprint detector. The success of an attack is assessed as the increment in the error rate of the SCI system. The PRNU-based SCI system was taken from an outstanding reference that is publicly available. Among the results of this work, the following are remarkable: the use of a systematic and extensive procedure to test SCI methods, very thorough testing of PRNU with more than 2000 test images, and the finding of some very effective attacks on PRNU-based SCI.
Introducing Amazon Explore: a digital giant's exploration of the virtual tourism experiences

Purpose Amazon Explore is a new online experience product from Amazon, which offers live stream sightseeing of destinations around the world. This paper aims to provide a first insight into how exactly Amazon Explore enters the virtual experience (VE) industry, and discuss the possible implications of its business model for the future of virtual and offline travel experiences. Design/methodology/approach Qualitative and quantitative methods of content analysis methods were employed in this study. The sample of this research consisted of 175 Amazon Explore products. Firstly, the authors analysed the content of each product, and then quantitative method were used to present the data. With this analysis, the authors presented how Amazon Explore is testing the foundations for new business models, and discuss the possible implications for the future of tourism. One year later, those same Amazon Explore products were reviewed again to check development progress. The following were the main questions regarding VE: How interactive are Amazon Explore products? How does Amazon Explore aim to ensure the monetisation of these products in an economically sustainable way in order to go beyond the “freemium” business model, often associated with VE as just another destination marketing tool? Findings This article presents the discussions of VE and virtual reality (VR). The authors determined the level of interactivity of the diverse VE offered themes with a model of four levels of interactivity. The results show that the analysed products achieve mostly only low levels of interaction. Regarding monetisation, Amazon Explore shows three possible future directions in the development of VE: as a marketing tool, as an extension of the offline experience and as potentially someday replacing real travel as part of the future monetisation scenario. Originality/value The article offers the first insights into Amazon's extension into the realm of VE in two time periods in 2020 and 2021, and, based on these preliminary results, discusses four possible scenarios for the future development of VE monetisation.
Analytical Formulation of the Second-Order Derivative of Energy for the Orbital-Optimized Variational Quantum Eigensolver: Application to Polarizability

We develop a quantum-classical hybrid algorithm to calculate the analytical second-order derivative of the energy for the orbital-optimized variational quantum eigensolver (OO-VQE), which is a method to calculate eigenenergies of a given molecular Hamiltonian by utilizing near-term quantum computers and classical computers. We show that all quantities required in the algorithm to calculate the derivative can be evaluated on quantum computers as standard quantum expectation values without using any ancillary qubits. We validate our formula by numerical simulations of quantum circuits for computing the polarizability of the water molecule, which is the second-order derivative of the energy with respect to the electric field. Moreover, the polarizabilities and refractive indices of thiophene and furan molecules are calculated as a testbed for possible industrial applications. We finally analyze the error-scaling of the estimated polarizabilities obtained by the proposed analytical derivative versus the numerical one obtained by the finite difference. Numerical calculations suggest that our analytical derivative requires fewer measurements (runs) on quantum computers than the numerical derivative to achieve the same fixed accuracy.
Automatic Identification of Seismic Signal Based on Entropy Feature and Genetic Algorithm Optimized BP Neural Network

In order to improve the accuracy of seismic event identification, a new method of applying distribution entropy, fuzzy entropy, and sample entropy to identify seismic waves is proposed, and the back propagation(BP) neural network optimized by genetic algorithm (GA) is used to test the identification effect. The seismic event identification effect is measured based on AUC, recall, accuracy, specificity and other indicators by applying some waveform records of the 2021 Qinghai Mado Ms7.4 earthquake and Yunnan Yangbi Ms6.4 and Ms5.6 earthquake. The results show that the combined criterion feature is better than a single criterion in the recognition effect. For a single criterion, the distinguishing ability of sample entropy is the best; the overall recognition effect of GA-BP is better than Boosting, decision tree, naive Bayes, et al. Finally, GA-BP model can be considered as an automatic identification method for identifying seismic waveform.
Person Re-Identification Based on Feature Fusion in AI System

In recent years, jointly utilizing local and global features to improve model performance is becoming an important approach for person re-identification. If the relationship between body parts is not considered, it is easy to confuse the identity differentiation of different persons with similar attributes in the corresponding parts. To solve this problem, we propose a feature fusion-based method for person re-identification, which contains three core parts: an adjacency module, a counterfactual attention module and a global difference pooling module. First, an adjacency module is designed to consider the relationship between adjacent body parts and make the features more discriminative. Next, a counterfactual attention module is proposed to conduct counterfactual intervention analysis and encourage the network to learn more useful attention to obtain more fine-grained features. Then, a global difference pooling module is used to learn the global features of a person’s image itself and pay more attention to the important features of the human body. Through the fusion of local features and global features, our model can effectively distinguish the identities of different people with similar attributes in the corresponding parts. Finally, we conduct a large number of experiments and achieve outstanding results on Market-1501, CUHK03 and Msmt17.
Blockchain in HRM:Application and Future Perspective

To effectively perform some of the basic HR activities, HR professionals are equipping themselves with cutting-edge technologies. HR staff deal with some of the financial transactions with the highest volume in a business. Utilizing blockchain technology can help stop internal and external hacks of private data. We looked at articles from the Scopus and Web of Science databases that were published between 2018 and 2022. Literature review shows that as more companies suffer from data breaches, it is critical that steps be taken to prevent fraud and maintain security. Blockchain technology may be used to help a void internal and external hack of private data. For aerospace and defense firms, Lockheed Martin employs it for risk management and software security. The use of blockchain technology offers the ability to automate many manual procedures and speed up processing.
MAR: <u>M</u>asked Autoencoders for Efficient <u>A</u>ction <u>R</u>ecognition

Standard approaches for video action recognition usually operate on full input videos, which is inefficient due to the widespread spatio-temporal redundancy in videos. The recent progress in masked video modelling, specifically VideoMAE, has shown the ability of vanilla Vision Transformers (ViT) to complement spatio-temporal contexts using limited visual content. Inspired by this, we propose Masked Action Recognition (MAR), which reduces redundant computation by discarding a proportion of patches and operating only on a portion of the videos. MAR includes two essential components: <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">cell running masking</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">bridging classifier</i> . Specifically, to enable the ViT to perceive the details beyond the visible patches, cell running masking is used to preserve the spatio-temporal correlations in videos. This ensures that the patches at the same spatial location can be observed in turn for easy reconstructions. Additionally, we notice that, although the partially observed features can reconstruct semantically explicit invisible patches, they fail to achieve accurate classification. To address this issue, we propose a bridging classifier that can help fill the semantic gap between the ViT encoded features used for reconstruction and the specialized features used for classification. Our proposed MAR can reduce the computational cost of ViT by 53%. Extensive experiments have demonstrated that MAR consistently outperforms existing ViT models by a notable margin. Notably, we found that a ViT-Large model fine-tuned by MAR achieves comparable performance to a ViT-Huge model fine-tuned by standard training methods on both Kinetics-400 and Something-Something v2 datasets. Moreover, the computation overhead of our ViT-Large model is only 14.5% of that of the ViT-Huge model. Codes have been made available <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/alibaba-mmai-research/Masked-Action-Recognition</uri> .
Quantum Secure Undeniable Signature for Blockchain-Enabled Cold-Chain Logistics System

Data security and user privacy are two main security concerns in the cold-chain logistics system (CCLS). Many security issues exist in traditional CCLS, destroying data security and user privacy. The digital signature can provide data verification and identity authentication based on the mathematical difficulty problem for logistics data sharing in CCLS. This paper first established a blockchain-enabled cold-chain logistics system (BCCLS) based on union blockchain technology, which can provide secure data sharing among different logistics nodes and guarantee logistics data security with the untampered blockchain ledger. Meanwhile, a lattice-based undeniable signature scheme is designed to strengthen the security of logistics data and user privacy against quantum attacks. This scheme is based on the lattice assumption, which can provide anti-quantum security for BCCLS in the future quantum computer age. It also establishes the undeniable mechanism that guarantees that the actual signer cannot deny a valid signature. Then, security proof shows that this undeniable signature scheme is correct and safe, and an efficiency comparison shows that it is more efficient than other schemes in similar literature. The performance evaluations of transaction throughput and latency show that the proposed BCCLS is efficient and practical. This work can improve the security and efficiency of logistics data management and promote the reform and development of the logistics industry.