Reviewerly/exHarmony huggingface dataset:
Optimum value of scale and threshold for compression of 99mTc-MDP bone scan images using Haar wavelet transform

Wavelet transforms of an image result in set of wavelet coefficients. Thresholding eliminates insigniﬁcant coefficients while retaining the significant ones (resulting in matrix having few nonzero elements that need to be stored). The compressed image is reconstructed by applying inverse wavelet transform. The quality of compressed image deteriorates with increase in compression. Hence, finding optimum value of scale and threshold is a challenging task. The objective of the study was to find the optimum value of scale and threshold for compressing 99mTc-methylene diphosphonate (99 mTc-MDP) bone scan images using Haar wavelet transform.Haar wavelet transform at scale 1-8 was applied on 106 99 mTc-MDP whole-body bone scan images, and wavelet coefficients were threshold at 90, 95, 97, and 99 percentiles, followed by inverse wavelet transform to get 3392 compressed images. Nuclear medicine physician (NMP) compared compressed image with its corresponding input to label it as acceptable or unacceptable. The values of scale and threshold that resulted in majority of acceptable images were considered to be optimum. The quality of compressed image was also evaluated using perception image quality evaluator (PIQE) image quality metrics. Compression ratio was calculated by dividing the number of nonzero elements after thresholding wavelet coefficients by the number of nonzero elements in Haar decomposed matrix.NMP found quality of compressed images (obtained at scale 2 and 90 percentile threshold) identical to the quality of the corresponding input images. As per PIQE score, quality of compressed images was perceptually better than that of the corresponding input images.The optimum values of scale and threshold were determined to be 2 and 90 percentiles, respectively.
LTReID: Factorizable Feature Generation With Independent Components for Long-Tailed Person Re-Identification

With the rapid increase of large-scale and real-world person datasets, it is crucial to address the problem of long-tailed data distributions, i.e., head classes have large number of images while tail classes occupy extremely few samples. We observe that the imbalanced data distribution is likely to distort the overall feature space and impair the generalization capability of trained models. Nevertheless, this long-tailed problem has been rarely investigated in previous person Re-Identification (ReID) works. In this paper, we propose a novel Long-Tailed Re-Identification (LTReID) framework to simultaneously alleviate class-imbalance and hard-imbalance problems. Specifically, each real feature is decomposed into multiple independent components with two decorrelation losses. Then these components are randomly aggre- gated to generate more fake features for tail classes than head ones, resulting in the class-balance between head and tail classes. For the hard-balance between easy and hard samples, we utilize adversarial learning to generate more hard features than easy ones. The proposed framework can be trained in an end-to-end manner and avoids increasing the space and time complexity of inference models. Moreover, comprehensive experiments are conducted on the four ReID datasets so as to validate the effectiveness of the overall framework and the advantage of each module. Our results show that when trained with either balanced or imbalanced datasets, the LTReID achieves superior performance over the state-of-the-art methods.
Attention Analysis and Calibration for Transformer in Natural Language Generation

Attention mechanism has been ubiquitous in neural machine translation by dynamically selecting relevant contexts for different translations. Apart from performance gains, attention weights assigned to input tokens are often utilized to explain that high-attention tokens contribute more to the prediction. However, many works question whether this assumption holds in text classification by manually manipulating attention weights and observing decision flips. This article extends this question to Transformer-based neural machine translation, which heavily relies on cross-lingual attention to produce accurate translations but is relatively understudied in this context. We first design a mask perturbation model which automatically assesses each input&#x2019;s contribution to model outputs. We then test whether the token contributing most to the current translation receives the highest attention weight. We find that it sometimes does not, which closely depends on the entropy of attention weights, the syntactic role of the current generation, and language pairs. We also rethink the discrepancy between attention weights and word alignments from the view of unreliable attention weights. Our observations further motivate us to calibrate the cross-lingual multi-head attention by attaching more attention to indispensable tokens, whose removal leads to a dramatic performance drop. Empirical experiments on different-scale translation tasks and text summarization tasks demonstrate that our calibration methods significantly outperform strong baselines.
Reinforcement Learning Heuristic A*

In a graph search algorithm, a given environment is represented as a graph comprising a set of feasible system configurations and their neighboring connections. A path is generated by connecting the initial and goal configurations through graph exploration, whereby the path is often desired to be optimal or suboptimal. The computational performance of the optimal path generation depends on the avoidance of unnecessary explorations. Accordingly, heuristic functions have been widely adopted to guide the exploration efficiently by providing estimated costs to the goal configurations. The exploration is efficient when the heuristic functions estimate the optimal cost closely, which remains challenging because it requires a comprehensive understanding of the environment. However, this challenge presents the scope to improve the computational efficiency over the existing methods. Herein, we propose reinforcement learning heuristic A* (RLHA*), which adopts an artificial neural network as a learning heuristic function to closely estimate the optimal cost, while achieving a bounded suboptimal path. Instead of being trained by precomputed paths, the learning heuristic function keeps improving by using self-generated paths. Numerous simulations were performed to demonstrate the consistent and robust performance of RLHA* by comparing it with the existing methods.
The Role of Artificial Intelligence in Human Resources

The study aims to identify the Role of Artificial Intelligence (AI) in human resources (HR) especially after Corona Virus (19) pandemic. The study showed that human resources have become part of the heart of the organization, linking all parties together, relying on artificial intelligence has become a must in the era of transformation we are witnessing. The study also showed that examples include looking at the candidate’s experiences for the job and reading all the resumes submitted to choose the most suitable candidate, because recruitment is a long-term process, and it is not easy to undo, and the HR staff is united and their knowledge, where artificial intelligence provides a common summary that avoids them focusing on different and contradictory matters.
A new mathematical model for contrast enhancement in digital images

The aim of this work is to propose a new mathematical model for optimal contrast enhancement of a digital image. The main idea is to combine the Divide-and-Conquer strategy, and a reaction diffusion mathematical model to enhance the contrast, and highlight the information and details of the image, based on a new conception of the Sine-Cosine optimization algorithm. The Divide-and-Conquer technique is a suitable method for contrast enhancement with an efficiency that directly depends on the choice of weights in the decomposition subspaces. Methods: in this paper, a new algorithm has been used for the optimal selection of the weights considering the optimization of the enhancement measure (EME). Results: in order to evaluate the effectiveness of the proposed algorithm, experimental results are presented which show that the proposed hybridization technique is robustly effective and produces clear and high contrast images.
Non-neural Models Matter: a Re-evaluation of Neural Referring Expression Generation Systems

In recent years, neural models have often outperformed rule-based and classic Machine Learning approaches in NLG.These classic approaches are now often disregarded, for example when new neural models are evaluated.We argue that they should not be overlooked, since for some tasks, well-designed non-neural approaches achieve better performance than neural ones.In this paper, the task of generating referring expressions in linguistic context is used as an example.We examined two very different English datasets (WEBNLG and WSJ), and evaluated each algorithm using both automatic and human evaluations.Overall, the results of these evaluations suggest that rule-based systems with simple rule sets achieve on-par or better performance on both datasets compared to state-of-the-art neural REG systems.In the case of the more realistic dataset, WSJ, a machine learning-based system with well-designed linguistic features performed best.We hope that our work can encourage researchers to consider non-neural models in future.* Equal contribution.
Multitemporal Sampling Module for Real-Time Human Activity Recognition

Human activity recognition, which recognizes human activities from time-series signals collected by sensors, is an important task in human-centered intelligent systems such as in healthcare and smart vehicles. In these applications, rapid response of the system is necessary because critical events such as an elderly person falling or drowsy driving require immediate action. A straightforward approach to achieving this requirement is to reduce the amount of information the model must handle. To this end, traditional studies have attempted to abstract the original signal by sampling it with a pre-defined interval. However, it is difficult to achieve the best efficiency because the ideal sampling interval is unknown in advance. In this study, we propose a multi-temporal sampling module that allows the neural networks to consider multiple sampling intervals simultaneously. Experiments on four benchmark datasets showed that the proposed model achieved the best F1 score over seven conventional models under the computation budget of 10M multiply-accumulate operations. Especially, an experiment on PAMAP2 dataset demonstrated that the proposed model can achieve the best trade-off between efficiency and accuracy when the input signal is oversampled at a high sampling frequency. In addition, the proposed model achieved <inline-formula> <tex-math notation="LaTeX">$\sim 1,000 \times $ </tex-math></inline-formula> improvement with respect to model size compared to the conventional methods.
Reducing Memory Requirements of Quantum Optimal Control

Quantum optimal control problems are typically solved by gradient-based algorithms such as GRAPE, which suffer from exponential growth in storage with increasing number of qubits and linear growth in memory requirements with increasing number of time steps. These memory requirements are a barrier for simulating large models or long time spans. We have created a nonstandard automatic differentiation technique that can compute gradients needed by GRAPE by exploiting the fact that the inverse of a unitary matrix is its conjugate transpose. Our approach significantly reduces the memory requirements for GRAPE, at the cost of a reasonable amount of recomputation. We present benchmark results based on an implementation in JAX.
Visual Transformer-Based Models: A Survey

After Transformer was first proposed by Vaswani et al. [1] in 2017, Transformer model has revolutionized and become the dominant methods in the field of natural language processing (NLP) which has achieved significant achievement. Transformer was first applied to Computer Vision fields in 2020, which called Vision Transformer (ViT) proposed by Dosovitskiy et al. ViT achieved state-of-the-art on image classification tasks at that time. In the past two years, the proliferation of Transformer in CV proves the effectiveness and breakthrough in various tasks including image classification, object detection, segmentation and low-level image tasks. In this paper, we focus on a review of Transformer-based models improved by ViT and Transformer backbones which is suitable for all kinds of image-level tasks, analyzing their improvement mechanisms, strengths and weaknesses. Furthermore, we briefly introduce the effective improvement of self-attention mechanism. In the end of this paper, some prospects have been put forward for future development on basis of the above Transformer-based models.
Uncertainty Estimation and Reduction of Pre-trained Models for Text Regression

Abstract State-of-the-art classification and regression models are often not well calibrated, and cannot reliably provide uncertainty estimates, limiting their utility in safety-critical applications such as clinical decision-making. While recent work has focused on calibration of classifiers, there is almost no work in NLP on calibration in a regression setting. In this paper, we quantify the calibration of pre- trained language models for text regression, both intrinsically and extrinsically. We further apply uncertainty estimates to augment training data in low-resource domains. Our experiments on three regression tasks in both self-training and active-learning settings show that uncertainty estimation can be used to increase overall performance and enhance model generalization.
A Survey of Consensus Mechanism Based on Reputation Model

The reputation models may be used to evaluate the reputation of blockchain nodes, and the consensus mechanisms can use the reputation models to quantify the credibility or dependability of nodes. The consensus mechanisms and the reputation models have been coupled by researchers with the goal of improving the performance and security of consensus mechanisms. We summarized the consensus mechanism based on the reputation model (reputation consensus) in this paper. To begin, we've classified the many types of reputation consensuses and explained the benefits of each one. Second, we suggest a new performance assessment criterion for reputation consensuses based on the experimental section of the literatures, and we evaluate the performance of current reputation consensuses using the provided performance evaluation criterion. After that, we ran a statistical investigation on the security of reputation consensuses. Finally, we summed up the reputation consensuses in order to better grasp present research issues and future research directions.
Fuzzy RDF Knowledge Graph Embeddings Through Vector Space Model

Resource description framework (RDF) is a World Wide Web consortium recommendation and has been widely accepted for semantic data models (e.g., knowledge graphs). As such, a large amount of RDF data is readily available. Information in the real world is often uncertain, and uncertain RDF data models have been proposed and received increasing attention. Knowledge graph embedding is an effective way to perform vector projection of structured knowledge, after which we can infer a large-scale knowledge graph. Nowadays, knowledge graph embedding models are widely proposed, but most of them can only handle crisp information. In this article, we concentrate on modeling fuzzy RDF graphs in the vector space. We propose a fuzzy RDF knowledge graph embedding model (FRKGE), which can project the fuzzy entities and fuzzy relations with membership degree to the vector space. Our proposed model preserves the structure of the fuzzy RDF knowledge graph and the explicit expression of membership degree in the vector space, which provides a natural expression of fuzzy RDF tuples. Our experimental results demonstrate that the FRKGE model is competent for the task of representation learning for fuzzy RDF knowledge graphs.
Cognitive Engineering for AI: An Octave Drawing Test for Building a Mathematical Structure of a Subconscious Mind

Since the 1920s, drawing tests have been used to measure intelligence, maturity, and personality: in the later parts of the twentieth century, drawing tests were extended to estimate depression, schizophrenia, and the degree of Alzheimer's. However, despite extensive literature on cognitive engineering, there is no grammar as generic rules for explaining drawing tests. Most inferences are drawn based on experiences, intuitions, and medical test confirmations. Here, we propose a set of eight tests to correlate fundamental rules of subconscious minds of a human subject follows based on which it constructs logical constructs for solving a problem. Based on the studies carried out on human subjects, we have proposed invariants or conservations across human subjects to interpret the drawing results and build technologies. Thus far, cognitive technologies of subconscious minds were limited to explore criminal minds. Our test protocol reveals generic rules of subconscious minds for engineering cognitive AI machines.
Recommender systems for Personalized Work Instructions

The introduction of digital systems in manufacturing has increased the number of ways that assistant information reaches the human operators in assembly work spaces. Such digital systems can accustom to different degrees of support, based on the level of detail of the information and the used medium. As the production environment becomes increasingly flexible and the tasks are changing in a faster pace, a more agile way for recommendations of support or instructions is needed. This work will present and compare two ways to represent instructional support, namely, on-screen digital work instructions (DWI) and Augmented Reality (AR)- based instructions. Furthermore, in order to assist an operator with the appropriate instructional support, a recommender system (RS) is developed, which matches an operator’s proficiency, a task’s requirement and a level of instruction. A purely data-driven RS using Bayesian Probabilistic Matrix Factorization (BPMF) is compared with a skill-driven approach. The latter has the advantage that the recommended level of instruction is directly relatable to a skill-based operator proficiency metric. For the evaluation of the two instruction delivery approaches and for training and testing the RS, a group of 13 users were called to complete two industrial setups, alternating between the different instruction types. Both RS were able to outperform a naive baseline.
Black-Box Reward Attacks Against Deep Reinforcement Learning Based on Successor Representation

Although the deep reinforcement learning (DRL) technology has been widely adopted in various fields, it has become an important research hotspot to study the vulnerability of DRL for improving the robustness of DRL agents. The adversarial attack methods based on white-box models, where the adversary can access all the information of victims, have been intensively investigated. However, in most practical situations, the adversary cannot obtain the internal information of the victim’s neural network. Furthermore, for reward-based attacks, the agent can perform anomaly detection on the perturbed rewards to detect whether it has been attacked. In this paper, we propose a black-box attack method with corrupted rewards, which employs DRL exploration mechanisms to improve the effectiveness of attacking agents. The adversary builds a deep neural network in advance to learn the successor representation (SR) of each state. Then, the adversary can determine the timing of attacks and generate imperceptible adversarial perturbations based on the values of the SR. Experimental results show that the black-box attack algorithm based on SR proposed in this paper can effectively attack agents with fewer adversarial samples.
Supervised Learning-Based Data Classification and Incremental Clustering

Using supervised learning-based data classification and incremental clustering, an unknown example can be classified using the most common class among K-nearest examples. The KNN classifier claims, "Tell me who your neighbors are, and it will tell you who you are". The supervised learning-based data classification and incremental clustering technique is a simple yet powerful approach with applications in computer vision, pattern recognition, optical character recognition, facial recognition, genetic pattern recognition, and other fields. It's also known as a slacker learner because it doesn't develop a model to classify a given test tuple until the very last minute. When we say "yes" or "no," there may be an element of chance involved. However, the fact that a diner can recognise an invisible food using his senses of taste, flavour, and smell is highly fascinating. At first, there can be a brief data collection phase: what are the most noticeable spices, aromas, and textures? Is the flavour of the food savoury or sweet? This information can then be used by the diner to compare the bite to other items he or she has had in the past. Earthy flavours may conjure up images of mushroom-based dishes, while briny flavours may conjure up images of fish. We view the discovery process through the lens of a slightly modified adage: if it smells like a duck and tastes like a chicken, you're probably eating chicken. This is a case of supervised learning in action. Machine learning can benefit from supervised learning, which is a concept that can be applied to it (ML).
SGPT: A Generative Approach for SPARQL Query Generation From Natural Language Questions

SPARQL query generation from natural language questions is complex because it requires an understanding of both the question and the underlying knowledge graph (KG) patterns. Most SPARQL query generation approaches are template-based, tailored to a specific knowledge graph and require pipelines with multiple steps, including entity and relation linking. Template-based approaches are also difficult to adapt for new KGs and require manual efforts from domain experts to construct query templates. To overcome this hurdle, we propose a new approach, dubbed SGPT, that combines the benefits of end-to-end and modular systems and leverages recent advances in large-scale language models. Specifically, we devise a novel embedding technique that can encode linguistic features from the question which enables the system to learn complex question patterns. In addition, we propose training techniques that allow the system to implicitly employ the graph-specific information (i.e., entities and relations) into the language model&#x2019;s parameters and generate SPARQL queries accurately. Finally, we introduce a strategy to adapt standard automatic metrics for evaluating SPARQL query generation. A comprehensive evaluation demonstrates the effectiveness of SGPT over state-of-the-art methods across several benchmark datasets.