Reviewerly/exHarmony huggingface dataset:
Quantum metrology with an <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>N</mml:mi></mml:math> -qubit <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>W</mml:mi></mml:math> superposition state under noninteracting and interacting operations

We study the performance of an $N$-qubit $W$ superposition state composed of a $W$ state and its obverse in quantum metrology. Taking advantage of the general Ising-type Hamiltonian (including noninteracting and interacting operation), we analytically present the quantum Fisher information (QFI) of an $N$-qubit $W$ superposition state under different situations and then investigate its phase sensitivity. The results show that the phase sensitivity under noninteracting operation displays a crossover from the $W$ state to Greenberger-Horne-Zeilinger (GHZ) state, where it is same as $W$ state in the few-qubit case $(N\ensuremath{\le}6)$ but asymptotically equal to the GHZ state for large-qubit cases $(N\ensuremath{\gg}1)$. Interestingly, the 4-qubit $W$ superposition state is found to have the same sensitivity as the 4-qubit GHZ state. And the optimal measurement protocols are provided for ideal metrology. Under the phase-amplitude damping channel, the phase sensitivity of the $W$ superposition state (except for $N=3$) is ultimately decreased to the standard quantum limit, while it turns worse in a depolarizing channel. Finally, the tunable phase sensitivity under interacting operation is studied, and the general Heisenberg limit is surpassed with the increasing interaction strength $\ensuremath{\gamma}$. Meanwhile, a plateau of QFI and phase sensitivity is found for all large-qubit $W$ superposition states, which is similar to the study of the GHZ state and again verifies the common feature of GHZ-type states in quantum metrology.
Multiple Tendon-inspired Sensors for Hand Motion Detection

Abstract Realistic presentation by a virtual avatar and detection are critical in a motion tracking system. Specifically, because the hand is an essential body part that interacts with other objects or humans in a virtual environment, accurate behavior tracking in real time is required. In this study, we propose a hand motion detection glove using multiple tendon-inspired sensors. Remarkably, there are no sensors or electronics in the hand part of the glove. Piezoelectric sensors are positioned on the wrist part of the glove, which are connected to the tendon in the glove. The elongated piezoelectric sensors detect the angle from joint changes caused by motions of the hand. In addition, we design a case for installing multiple sensors on many joints of the fingers. To demonstrate the sensing ability of the bending motion of the finger, a series of sensor tests is conducted using a finger structure setup. We obtain sensing results with changes in the rotational angles and frequencies. Moreover, the rotational angle can be estimated from the sensing data. Based on these results, a virtual hand system following real hand motion is constructed utilizing a motion detection glove with multiple tendon-inspired sensors. Finally, we investigate the effects of Young’s modulus, number of threads, and the structure of the elastic thread to enhance the output of the sensors.
Knowledge reasoning with multiple relational paths

Knowledge reasoning technology can infer new knowledge based on the existing entity relationship information in the knowledge graph to complement the knowledge graph. In the existing knowledge reasoning methods based on multi-step relationship paths, the contributions of entities and relationships in the relationship paths are not distinguished, nor are multiple relationship paths comprehensively considered. To solve these problems, we propose a knowledge reasoning model based on Bi-directional Short-Term Memory and Attention Mechanism. Firstly, the neighborhood, entity semantic category and relationship path information are integrated in the knowledge graph, and the Neighborhood Semantic Path Network model (NSPN) is constructed to obtain a hybrid representation of the multi-step relationship path. Secondly, the loss function and training process of NSPN are introduced. Finally, comparative experiments are carried out on the public dataset, and the results show that our model is superior to other models in relation prediction tasks and link prediction tasks, improves the computational efficiency and data sparsity, and provides a new idea for knowledge reasoning methods.
Real-time object detection and tracking using YOLOv3 network by quadcopter

AbstractAbstractIn this article, artificial intelligence is applied for real-time object detection in Tello quadcopters. For this purpose, the YOLOv3 detection algorithm as a highly used deep-learning method is employed. The results indicate that the YOLOv3 network can be trained with an accuracy of 99 percent and can detect the target with above 95 percent accuracy at a speed of 15 frames-per-second for different ambient lighting and background conditions. The YOLOv3 algorithm is trained using a custom dataset and implemented in Python. Images are sent to a computer using Python language to detect the target and entered into the YOLOv3 algorithm. After detecting the target, the errors are calculated and given to the control system to track the target in real-time. There are two purposes in tracking, the target is in the view of the quadcopter, and the quadcopter is at a certain distance from the target. For this purpose, the effect of quadcopter movements on the coordinates and area of the target is examined and four controllers are designed to follow the target and keep the robot at a certain distance from the target. The designed controllers efficiently follow the target and prevent flying robots from losing sight of the target.Keywords: Flying robotYOLOv3 algorithmobject trackingtarget detection
Reversible data hiding in JPEG document images based on zero coefficients embedding

JPEG document images are widely used in business and identity occasions. Reversible data hiding (RDH) can ensure the authenticity and integrity of JPEG document images. Most existing JPEG RDH schemes embed data into the quantified DCT coefficients with ±1 value. However, in JPEG document images, the number of ±1 coefficients is far less than that of zero coefficients, and modifying ±1 coefficients may lead to greater visual distortions. In this paper, a high capacity RDH scheme in JPEG document images based on an advanced zero coefficients embedding strategy is proposed. Firstly, JPEG document images have obvious edges and simple textures, which provide a huge number of zero coefficients to embed data. By analyzing the mean square error, zero coefficients are the best options to maintain the visual quality. Then, by minimizing a well-constructed embedding cost, the optimal zero coefficients are selected to significantly decrease the visual distortion. Lastly, the shifting gap as the small-scale overhead information is recorded in advance, so that the DCT coefficients with a great value can be avoided modification. Experiments show that the zero coefficients embedding can significantly improve the visual quality, and the maximal embedding capacity is higher compared with state-of-the-art RDH in JPEG images.
Cooperative task scheduling secured with blockchain in sustainable mobile edge computing

The emergence and rapid growth of smart and IoT devices increased the demand for an efficient and effective task computing platform. Mobile edge computing (MEC) has emerged as a solution for the execution of these proximal and latency-sensitive tasks. Additionally, security is a concern with any computation platform containing distributed and heterogeneous devices. Blockchain is a recent technique that has emerged as a potential solution to provide authenticity, security, consistency, and integrity. The proposed work utilizes the advantages of blockchain technology to provide security with the scheduling of tasks. BSFW has been proposed for the secure scheduling of tasks that utilizes the features of the fireworks algorithm for the optimization of schedules. The objective function has been presented to optimize the makespan and energy consumption of the schedules. To evaluate the effectiveness of the proposed algorithm, different experiments have been conducted that results in the outperformance of the proposed work with other compared techniques in different scenarios. The result has provided 14–25% improvement in performance compared to other approaches. The evaluation has been performed on makespan, energy consumption, throughput, convergence, and execution time metrics.
VICTOR: Visual incompatibility detection with transformers and fashion-specific contrastive pre-training

For fashion outfits to be considered aesthetically pleasing, the garments that constitute them need to be compatible in terms of visual aspects, such as style, category and color. Previous works have defined visual compatibility as a binary classification task with items in a garment being considered as fully compatible or fully incompatible. However, this is not applicable to Outfit Maker applications where users create their own outfits and need to know which specific items may be incompatible with the rest of the outfit. To address this, we propose the Visual InCompatibility TransfORmer (VICTOR) that is optimized for two tasks: 1) overall compatibility as regression and 2) the detection of mismatching items and utilize fashion-specific contrastive language-image pre-training for fine tuning computer vision neural networks on fashion imagery. We build upon the Polyvore outfit benchmark to generate partially mismatching outfits, creating a new dataset termed Polyvore-MISFITs, that is used to train VICTOR. A series of ablation and comparative analyses show that the proposed architecture can compete and even surpass the current state-of-the-art on Polyvore datasets while reducing the instance-wise floating operations by 88%, striking a balance between high performance and efficiency. We release our code at https://github.com/stevejpapad/Visual-InCompatibility-Transformer
Weight of authorship evidence with multiple categories of stylometric features: A multinomial-based discrete model

This study empirically demonstrates the efficacy of a two-level Dirichlet-multinomial statistical model (the Multinomial system) for computing likelihood ratios (LR) for linguistic, textual evidence with multiple stylometric feature types with discrete values. The LRs are calculated separately for each feature type, namely, word, character and part of speech N-grams (N = 1,2,3), which are combined as overall LRs through logistic regression fusion. The Multinomial system's performance is compared with that of a previously proposed system with the cosine distance (the Cosine system) using the same data (i.e., documents collated from 2160 authors). The experimental results show that: (1) the Multinomial system outperforms the Cosine system with the fused feature types by a log-LR cost of ca. 0.01 ∼ 0.05 bits; and (2) the Multinomial system is more advantageous in performance with longer documents than the Cosine system. Although the Cosine system is more robust overall against the sampling variability arising from the number of authors included in the reference and calibration databases, the Multinomial system can achieve reasonable stability in performance; for example, the standard deviation value of the log-LR cost becomes lower than 0.01 (10 random samplings of authors for the reference and calibration databases) with 60 or more authors in each database.
Video coding deep learning-based modeling for long life video streaming over next network generation

Availability is one of the primary goals of smart networks, especially, if the network is under heavy video streaming traffic. In this paper, we propose a deep learning based methodology to enhance availability of video streaming systems by developing a prediction model for video streaming quality, required power consumption, and required bandwidth based on video codec parameters. The H.264/AVC codec, which is one of the most popular codecs used in video steaming and conferencing communications, is chosen as a case study in this paper. We model the predicted consumed power, the predicted perceived video quality, and the predicted required bandwidth for the video codec based on video resolution and quantization parameters. We train, validate, and test the developed models through extensive experiments using several video contents. Results show that an accurate model can be built for the needed purpose and the video streaming quality, required power consumption, and required bandwidth can be predicted accurately which can be utilized to enhance network availability in a cooperative environment.
Sphere Face Model: A 3D morphable model with hypersphere manifold latent space using joint 2D/3D training

Abstract 3D morphable models (3DMMs) are generative models for face shape and appearance. Recent works impose face recognition constraints on 3DMM shape parameters so that the face shapes of the same person remain consistent. However, the shape parameters of traditional 3DMMs satisfy the multivariate Gaussian distribution. In contrast, the identity embeddings meet the hypersphere distribution, and this conflict makes it challenging for face reconstruction models to preserve the faithfulness and the shape consistency simultaneously. In other words, recognition loss and reconstruction loss can not decrease jointly due to their conflict distribution. To address this issue, we propose the Sphere Face Model (SFM), a novel 3DMM for monocular face reconstruction, preserving both shape fidelity and identity consistency. The core of our SFM is the basis matrix which can be used to reconstruct 3D face shapes, and the basic matrix is learned by adopting a two-stage training approach where 3D and 2D training data are used in the first and second stages, respectively. We design a novel loss to resolve the distribution mismatch, enforcing that the shape parameters have the hyperspherical distribution. Our model accepts 2D and 3D data for constructing the sphere face models. Extensive experiments show that SFM has high representation ability and clustering performance in its shape parameter space. Moreover, it produces high-fidelity face shapes consistently in challenging conditions in monocular face reconstruction. The code will be released at https://github.com/a686432/SIR
Polygonal finite element-based content-aware image warping

Abstract Mesh-based image warping techniques typically represent image deformation using linear functions on triangular meshes or bilinear functions on rectangular meshes. This enables simple and efficient implementation, but in turn, restricts the representation capability of the deformation, often leading to unsatisfactory warping results. We present a novel, flexible polygonal finite element (poly-FEM) method for content-aware image warping. Image deformation is represented by high-order poly-FEMs on a content-aware polygonal mesh with a cell distribution adapted to saliency information in the source image. This allows highly adaptive meshes and smoother warping with fewer degrees of freedom, thus significantly extending the flexibility and capability of the warping representation. Benefiting from the continuous formulation of image deformation, our poly-FEM warping method is able to compute the optimal image deformation by minimizing existing or even newly designed warping energies consisting of penalty terms for specific transformations. We demonstrate the versatility of the proposed poly-FEM warping method in representing different deformations and its superiority by comparing it to other existing state-of-the-art methods.
A policy optimization algorithm based on sample adaptive reuse and dual-clipping for robotic action control

When applying deep reinforcement learning in the real physical environment for decision-making, how to improve the sample efficiency while ensuring training stability is an urgent problem that needs to be solved. In order to solve this problem, some of on-policy algorithms are proposed and have achieved state-of-the-art performance. However, these on-policy algorithms, such as proximal policy optimization (PPO) algorithm, have the drawback of extremely low sample efficiency. In this study, we proposed a novel policy optimization method named improved proximal policy optimization algorithm based on sample adaptive reuse and dual-clipping (SARD-PPO) for robotic action control, which combines the advantage of the on-policy methods in training stability with the advantage of the off-policy methods in sample efficiency. First, we analyzed the clipping mechanism of the PPO algorithm, devised a more constrained clipping mechanism based on the analysis of the relationship between the clipping mechanism and the objective constraints, and developed a policy updating method that reuses the old samples of the prior policy in a more principle-based way. Second, we ensured the training stability of the algorithm through element-level dual-clipping, as well as adaptive adjustment and reuse of the entire policy trajectory. The experimental results on six tasks in the MuJoCo benchmark indicate that SARD-PPO can significantly improve policy performance while balancing policy training stability and sample efficiency, outperforming the baseline PPO algorithm and other SOTA policy gradient methods using on- and off-policy samples in terms of overall performance.
GUBS criterion: Arbitrary trade-offs between cost and probability-to-goal in stochastic planning based on Expected Utility Theory

Stochastic Shortest Path MDPs (SSP-MDPs) are used to model probabilistic sequential decision problems where the objective is to minimize the expected accumulated cost to goal. However, in the presence of dead-ends, the conventional criterion for SSP-MDPs, which minimizes the expected accumulated cost, can become ill-defined. Lexicographic criteria can solve this by preferring policies that reach the goal with the highest possible probability. Other criteria can instead make a trade-off between some cost measure and probability-to-goal. However, both of these approaches can lead to policies that might not represent the choice of a real decision-maker. In this work, we propose the GUBS criterion to address these problems. GUBS combines goal prioritization over histories with Expected Utility Theory and is the only criterion between all criteria analyzed that not only allows for a trade-off between a large accumulated cost and a small loss in probability-to-goal, but also guarantees arbitrary trade-offs that can be tuned from its parameters without previous knowledge of the problem being solved. We also propose eGUBS, which is a particular case of GUBS when the exponential utility function is used, and two algorithms for optimally solving these problems: eGUBS-VI, a VI-based algorithm; and eGUBS-AO*, a heuristic search algorithm. Results indicate that, when there is a good heuristic function available or when the state space is too large, eGUBS-AO* can perform better than eGUBS-VI by doing an efficient search. In other cases, eGUBS-VI's simpler approach might have better results.
Fairness–accuracy tradeoff: activation function choice in a neural network

Models can have different outcomes based on the different types of inputs; the training data used to build the model will change its output (a data-centric outcome), while the hyperparameter selection can also affect the model’s output and performance (a model-centric outcome). When building classification models, data scientists generally focus on model performance as the key metric, often focusing on accuracy. However, other metrics should be considered during model development that assesses performance in other ways, like fairness. Assessing the fairness during the model development process is often overlooked but should be considered as part of a model’s full assessment before deployment. This research investigates the fairness–accuracy tradeoff that occurred by changing only one hyperparameter in a neural network for binary classification to assess how this single change in the hidden layer can alter the algorithm’s accuracy and fairness. Neural networks were used for this assessment because of their wide usage across domains, limitations to current bias measurement methods, and the underlying challenges in their interpretability. Findings suggest that assessing accuracy and fairness during model development provides value while mitigating potential negative effects for users while reducing organizational risk. No particular activation function was found to be fairer than another. Notable differences in the fairness and accuracy measures could help developers deploy a model with high accuracy and robust fairness. Algorithm development should include a grid search for hyperparameter optimization that includes fairness along with performance measures, like accuracy. While the actual choices for hyperparameters may depend on the business context and dataset considered, an optimal development process should use both fairness and model performance metrics.
Machine Learning for Face Recognition in Shallow Data

Face recognition has been widely studied in the artificial intelligence field. Especially, with the increasing availability of machine learning algorithms such as deep learning, most existing research uses massive datasets to facilitate better recognition performance. However, it is time-consuming and labor-intensive to collect and annotate a large face dataset. On the contrary, with a small dataset, some of the machine learning algorithms may fail to perform the recognition task. In order to study the performances of different machine learning algorithms, this work compares the recognition performances of seven widely used machine learning algorithms on three different face datasets. With shallow data, the experimental results reveal that the best recognition accuracy on different datasets can be achieved by different algorithms. Specifically, the best performance of 66.41% on the Extended Yale B dataset is achieved by the logistic regression algorithm, the LDA algorithm achieves the best performance of 97.5% on the Olivetti face dataset, while the random forest algorithm obtains the best accuracy of 10.31% on the LFW face dataset, with five shallow training samples. The obtained results can be used to select appropriate ML algorithms to recognize faces with shallow data.
Lexical Networks Constructed to Correspond to Students’ Short Written Responses: A Quantum Semantic Approach

A simple method to construct lexical networks (lexicons) of how students use scientific terms in written texts is introduced. The method is based on a recently introduced quantum semantics generalization of a word-pair co-occurrence. Quantum semantics allows entangled co-occurrence, thus allowing to model the effect of subjective bias on weighting the importance of word co-occurrence. Using such a generalized word-pair co-occurrence counting, we construct students’ lexicons of scientific (life-science) terms they use in their written responses to questions concerning food chains in life-science contexts. The method allows us to construct ensembles of lexicons that probabilistically simulate the variability of individual lexicons. The re-analyses of the written reports show that while sets of top-ranking terms contain nearly the same terms irrespective of details of the method used to count co-occurrences, the relative rankings of some key-terms may be different in quantum semantic analysis.