Reviewerly/exHarmony huggingface dataset:
Random Maximum 2 Satisfiability Logic in Discrete Hopfield Neural Network Incorporating Improved Election Algorithm

Real life logical rule is not always satisfiable in nature due to the redundant variable that represents the logical formulation. Thus, the intelligence system must be optimally governed to ensure the system can behave according to non-satisfiable structure that finds practical applications particularly in knowledge discovery tasks. In this paper, we a propose non-satisfiability logical rule that combines two sub-logical rules, namely Maximum 2 Satisfiability and Random 2 Satisfiability, that play a vital role in creating explainable artificial intelligence. Interestingly, the combination will result in the negative logical outcome where the cost function of the proposed logic is always more than zero. The proposed logical rule is implemented into Discrete Hopfield Neural Network by computing the cost function associated with each variable in Random 2 Satisfiability. Since the proposed logical rule is difficult to be optimized during training phase of DHNN, Election Algorithm is implemented to find consistent interpretation that minimizes the cost function of the proposed logical rule. Election Algorithm has become the most popular optimization metaheuristic technique for resolving constraint optimization problems. The fundamental concepts of Election Algorithm are taken from socio-political phenomena which use new and efficient processes to produce the best outcome. The behavior of Random Maximum 2 Satisfiability in Discrete Hopfield Neural Network is investigated based on several performance metrics. The performance is compared between existing conventional methods with Genetic Algorithm and Election Algorithm. The results demonstrate that the proposed Random Maximum 2 Satisfiability can become the symbolic instruction in Discrete Hopfield Neural Network where Election Algorithm has performed as an effective training process of Discrete Hopfield Neural Network compared to Genetic Algorithm and Exhaustive Search.
Deterministic generation of multi-photon bundles in a quantum Rabi model

Multi-photon bundle states are crucial for a broad range of applications such as quantum metrology, quantum lithography, quantum communication, and quantum biology. Here we propose a scheme that generates multi-photon bundles via virtual excitations in a quantum Rabi model. Our approach utilizes a Ξ-type three-level atom, where the upper two levels are coupled to a cavity field to form a quantum Rabi model with ultrastrong coupling, and the transition between the lower two levels is driven by two sequences of Gaussian pulses. We show that the driving pulses induce deterministic emission of multiple photons from the eigenstates of the quantum Rabi model via the stimulated Raman adiabatic passage technique, and hence can create bundles of multiple photons on-demand in the cavity output field. We calculate the generalized second-order correlation functions of the output photons, which reveal that the emitted photons form antibunched multi-photon bundles.
Mapping hydrothermal and supergene alteration zones associated with carbonate-hosted Zn-Pb deposits by using PRISMA satellite imagery supported by field-based hyperspectral data, mineralogical and geochemical analysis

Delineating hydrothermal alteration and supergene caps is fundamental for mineral exploration of sulfide ores. The aim of this study is to apply a multi-scale workflow based on hyperspectral remote and proximal sensing data in order to delineate hydrothermal dolomitization and supergene alteration associated with the Mississippi Valley-Type Zn-Pb(-Ag) deposit of Jabali (Western Yemen). The area was investigated through hyperspectral images derived from the new launched Italian Space Agency’s PRISMA satellite, which has a higher spectral resolution compared to multispectral sensors and covers the mineral-diagnostic wavelength regions (such as the 2100 nm to 2300 nm range) with a Signal to Noise Ratio (SNR) ≥ 100. Spectral mineral maps were produced through the band ratios method using specific feature extraction indices applied to the hyperspectral satellite data. The results were validated by using Visible Near InfraRed (VNIR) to Short Wave InfraRed (SWIR) reflectance spectra, mineralogical (XRPD) and geochemical (ICP-ES/MS) analyses on rock samples collected in the Jabali area. The dolomites footprint was mapped using a PRISMA Level 2C image, by enhancing the spectral differences between limestones and dolomites in the SWIR-2 region (major features centered at 2340 nm and 2320 nm, respectively). Gossans were detected due to the Fe3+ absorption band in the VNIR region at 900 nm. The Zn-Pb mineralized area, extended for approximately 25 km2, was thus identified by recognizing gossan occurrences in dolomites. The study demonstrates that the PRISMA satellite is effective in identifying Zn-Pb mineralized outcrops in sedimentary basins.
Double-track particle swarm optimizer for nonlinear constrained optimization problems

Particle swarm optimization (PSO) is a popular stochastic algorithm for solving nonlinear optimization problems. It has been used in various industrial applications because of its few parameters and ease of implementation. Various optimal problems must be frequently formed as nonlinear constrained optimization (NCO) problems because of global resource limitations. These problem-specific nonlinear constraints can result in many infeasible regions in the search space. Current PSO-based approaches are inherently designed for unconstrained optimization problems and cannot directly handle the constraint issue; therefore, they add the constraint handling technique (CHT) to account for NCO problems. This study proposes a double-track schema inside PSO called double-track PSO (DTPSO) to address the NCO problem. When an infeasible region blocks a particle, the DTPSO creates a copy of the particle. The original and copied particles utilize different search strategies to move to feasible and infeasible regions. The results indicate that the proposed DTPSO outperforms the seven referenced PSO-based methods in all 33 NCO problems and five recent strong methods in 19 mechanical design problems belonging to the IEEE CEC 2020 competition on real-world single-objective constrained optimization problems. Furthermore, the experiment results demonstrate the quality of the solution and the effective computation of the proposed DTPSO.
Towards more efficient local search algorithms for constrained clustering

Constrained clustering extends clustering by integrating user constraints, and aims to determine an optimal assignment under the constraints. In this paper, we propose a local search algorithm called FastCCP to solve the constrained clustering problem. In the algorithm, instances connected by must-link constraints are first merged into nodes, and then, a local search method is performed to handle the cannot-link constraints while minimizing the Within-Cluster Sum of Squares (WCSS). Several strategies are proposed to enhance the solution diversity and achieve a trade-off between constraint satisfaction and WCSS minimization during the search. Furthermore, a node-filtering strategy is proposed to improve the efficiency of the algorithm. Experiments are performed on benchmark datasets to evaluate our algorithm. The comparative results indicate that our algorithm outperforms state-of-the-art algorithms in terms of both the solution quality and CPU runtime.
Knowledge Engineering in Chemistry: From Expert Systems to Agents of Creation

Passing knowledge from human to human is a natural process that has continued since the beginning of humankind. Over the past few decades, we have witnessed that knowledge is no longer passed only between humans but also from humans to machines. The latter form of knowledge transfer represents a cornerstone in artificial intelligence (AI) and lays the foundation for knowledge engineering (KE). In order to pass knowledge to machines, humans need to structure, formalize, and make knowledge machine-readable. Subsequently, humans also need to develop software that emulates their decision-making process. In order to engineer chemical knowledge, chemists are often required to challenge their understanding of chemistry and thinking processes, which may help improve the structure of chemical knowledge.Knowledge engineering in chemistry dates from the development of expert systems that emulated the thinking process of analytical and organic chemists. Since then, many different expert systems employing rather limited knowledge bases have been developed, solving problems in retrosynthesis, analytical chemistry, chemical risk assessment, etc. However, toward the end of the 20th century, the AI winters slowed down the development of expert systems for chemistry. At the same time, the increasing complexity of chemical research, alongside the limitations of the available computing tools, made it difficult for many chemistry expert systems to keep pace.In the past two decades, the semantic web, the popularization of object-oriented programming, and the increase in computational power have revitalized knowledge engineering. Knowledge formalization through ontologies has become commonplace, triggering the subsequent development of knowledge graphs and cognitive software agents. These tools enable the possibility of interoperability, enabling the representation of more complex systems, inference capabilities, and the synthesis of new knowledge.This Account introduces the history, the core principles of KE, and its applications within the broad realm of chemical research and engineering. In this regard, we first discuss how chemical knowledge is formalized and how a chemist's cognition can be emulated with the help of reasoning algorithms. Following this, we discuss various applications of knowledge graph and agent technology used to solve problems in chemistry related to molecular engineering, chemical mechanisms, multiscale modeling, automation of calculations and experiments, and chemist-machine interactions. These developments are discussed in the context of a universal and dynamic knowledge ecosystem, referred to as The World Avatar (TWA).
A Novel Degraded Document Binarization Model through Vision Transformer Network

Degraded document binarization has received keen attention due to its vital influence on subsequent document analysis tasks. In this study, we propose a novel Degraded Document Binarization model through the vision transFormer framework, termed D2BFormer. Thanks to its end-to-end trainable fashion, the D2BFormer model is able to autonomously optimize its parameterized configuration of the entire learning pipeline without incurring the intensity-to-binary value conversion phase, resulting in an improved binarization quality. In addition, we propose a novel dual-branched encoding feature fusion module, which combines architectural components from the vision transformer framework and deep convolutional neural networks. The resulting encoding module can extract features from an input document that are sensitive to both global and local characteristics. Meanwhile, the proposed encoding feature extraction module can operate internally at a much lower spatial resolution than that of a raw input document, leading to reduced computational complexity. Furthermore, we propose a novel progressively merged decoding feature fusion module through carefully introduced skip connections both inside and outside the decoding network. The resulting decoding module progressively combines counterpart features derived from the corresponding layers of the encoding network with comparable spatial resolutions and up-sampled features generated from previous layers in the decoding network. Finally, the experiments conducted on ten public datasets demonstrate that the proposed D2BFormer model gains promising performance in terms of four metrics.
U-SPDNet: An SPD manifold learning-based neural network for visual classification

With the development of neural networking techniques, several architectures for symmetric positive definite (SPD) matrix learning have recently been put forward in the computer vision and pattern recognition (CV&PR) community for mining fine-grained geometric features. However, the degradation of structural information during multi-stage feature transformation limits their capacity. To cope with this issue, this paper develops a U-shaped neural network on the SPD manifolds (U-SPDNet) for visual classification. The designed U-SPDNet contains two subsystems, one of which is a shrinking path (encoder) making up of a prevailing SPD manifold neural network (SPDNet (Huang and Van Gool, 2017)) for capturing compact representations from the input data. Another is a constructed symmetric expanding path (decoder) to upsample the encoded features, trained by a reconstruction error term. With this design, the degradation problem will be gradually alleviated during training. To enhance the representational capacity of U-SPDNet, we also append skip connections from encoder to decoder, realized by manifold-valued geometric operations, namely Riemannian barycenter and Riemannian optimization. On the MDSD, Virus, FPHA, and UAV-Human datasets, the accuracy achieved by our method is respectively 6.92%, 8.67%, 1.57%, and 1.08% higher than SPDNet, certifying its effectiveness.
The effects of spatial configuration on relative translation gain thresholds in redirected walking

In this study, we explore how spatial configurations can be reflected in determining the threshold range of Relative Translation Gains (RTGs), a translation gain-based Redirected Walking (RDW) technique that scales the user's movement in Virtual Reality (VR) in different ratios for width and depth. While previous works have shown that various cognitive factors influence the RDW threshold, studies investigating the impact of environmental aspects on the RDW threshold with regard to the user's visual perception were lacking. Therefore, we examined the effect of spatial configurations on the RTG threshold by analyzing the participant's responses and gaze distribution data in two user studies. The first study concerned the size of the virtual room and the existence of objects within it, and the second study focused on the combined impact of room size and spatial layout. Our results show that room size, the existence of objects, and spatial layout all significantly affect the RTG threshold range. Based on our findings, we propose virtual space rescaling guidelines to increase the range of adjustable movable space with RTGs for developers: placing distractors in the room, setting the perceived movable space to be larger than the adjusted movable space for an empty room, and avoiding the placement of object clusters in the center of the room. Our findings can be used to adaptively rescale VR users' space according to the target virtual space's configuration with a unified coordinate system that enables the utilization of physical objects in a virtual scene.
Textual emotion detection in health: Advances and applications

Textual Emotion Detection (TED) is a rapidly growing area in Natural Language Processing (NLP) that aims to detect emotions expressed through text. In this paper, we provide a review of the latest research and development in TED as applied in health and medicine. We focus on medical and non-medical data types, use cases, and methods where TED has been integral in supporting decision-making. The application of NLP technologies in health, and particularly TED, requires high confidence that these technologies and technology-aided treatment will first, do no harm. Therefore, this review also aims to assess the accuracy of TED systems and provide an update on the state of the technology. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines were used in this review. With a specific focus on the identification of different human emotions in text, the more general sentiment analysis studies that only recognize the polarity of text were excluded. A total of 66 papers met the inclusion criteria. This review found that TED in health and medicine is mainly used in the detection of depression, suicidal ideation, and the mental status of patients with asthma, Alzheimer's disease, cancer, and diabetes with major data sources of social media, healthcare services, and counseling centers. Approximately, 44% of the research in the domain is related to COVID-19, investigating the public health response to vaccinations and the emotional response of the public. In most cases, deep learning-based NLP techniques were found to be preferred over other methods due to their superior performance. Developing methods for implementing and evaluating dimensional emotional models, resolving annotation challenges by utilizing health-related lexicons, and using deep learning techniques for multi-faceted and real-time applications were found to be among the main avenues for further development of TED applications in health.
Optimal Operation Policies in a Cross-Regional Fresh Product Supply Chain with Regional Government Subsidy Heterogeneity to Blockchain-Driven Traceability

The quality of fresh products through cross-regional sales has been uncertain to consumers. To improve the quality of fresh products, some fresh product supply chains have implemented blockchain technology to provide traceable information for their products, and some regional governments have subsidized their local firms to incentivize them to implement blockchain-driven traceability systems. However, with regional government subsidy heterogeneity, cross-regional fresh product supply chain firms lack theoretical guidance on their operation decisions. Based on the research gap, we investigate optimal operation policies in a fresh product supply chain consisting of a manufacturer and a retailer located in different regions. The local governments may subsidize the manufacturer or the retailer located in their own regions, which construct four subsidy strategies (SS, SN, NS, and NN) along the supply chain. We find that the optimal operation policies under four subsidy strategies can be affected by the sensitivity to traceability level, cost-sharing rate of the manufacturer, rate of products left after corrosion, and subsidy rate to the manufacturer. Moreover, the government subsidy to the retailer is always beneficial to the retailer and the supply chain but does not affect the manufacturer’s operation policies and profits. The government subsidy to the manufacturer is always beneficial to the manufacturer but not always beneficial to the retailer and the supply chain. Hence the desired subsidy strategy for the manufacturer is SS and SN, and the one for the retailer and the supply chain is either NS or SS with different conditions.
Skill Scanner: Connecting and Supporting Employers, Job Seekers and Educational Institutions with an AI-Based Recommendation System

Usually employers, job seekers and educational institutions use AI in isolation from one another. However, skills are the common ground between these three parties which can be analyzed with the help of AI. Employers want to automatically check which of their required skills are covered by applicants' CVs and know which courses their employees can take to acquire missing skills. Job seekers want to know which skills from job postings are missing in their CV and which study programs they can take to acquire missing skills. In addition, educational institutions want to make sure that skills required in job postings are covered in their curricula, and they want to recommend study programs. Consequently, we investigated several natural language processing techniques to extract, vectorize, cluster and compare skills, thereby connecting and supporting employers, job seekers and educational institutions. Our application Skill Scanner uses our best algorithms and outputs statistics and recommendations for all groups. The results of our survey demonstrate that the majority finds that with the help of Skill Scanner, processes related to skills are carried out more effectively, faster, fairer, more explainably, and in a more supported manner. In total, 89% of all participants are not averse to apply our recommendation system for their tasks, and 67% of job seekers would certainly use it.
Fuzzy rule-based anomaly detectors construction via information granulation

It is meaningful for data-driven studies to detect and remove the anomaly data from a given raw dataset. Considering the existing anomaly data detection (ADD) methods have some drawbacks, e.g. confidence and uncertainty in anomaly description, computation cost of kernel learning and deep learning in ADD, therefore this paper proposes an advanced approach for constructing low-cost and effective ADD detectors. Firstly, making use of the good data description ability of information granules, granular data descriptors are constructed for anomaly and normal data description. Then, based on these data descriptors, reconstruction-based strategy is applied to model anomaly detection. Subsequently, with consideration of anomaly data’s uncertainty, fuzzy rules are formed and used to realize the final anomaly data detection. In this study, both synthetic and publicly available datasets are considered in experimental analysis. Numerical results illustrate the proposed method has a superiority ranging from 8.60% to 32.58% to conventional models on ADD performance, verifying the feasibility and effectiveness the proposed methods via granular data descriptors. Moreover, through the discussion on computation complexity, the proposed method is also demonstrated efficient on detecting anomalies.
A deep generative multimodal imaging genomics framework for Alzheimer's disease prediction

Alzheimer's disease (AD) is a neurodegenerative process characterized by the accumulation of amyloid-beta plaques and neurofibrillary tangles and is the most common cause of dementia. Studies have been striving to analyze the disease using available physiological and behavioral data. Functional/structural neuroimaging and genomics are complementary modalities for exploring the mechanisms subserving the development of AD. In this paper, we present a deep multimodal generative data fusion framework for integrating these sources in a classification task involving AD patients and healthy controls from the ADNI database. Biological data fusion has the potential to improve the final prediction, but at the same time, it is particularly challenging due to the unavailability of all sources of input for the entire cohort of subjects. Our proposed model allows us to perform prediction even if individuals are missing certain modalities. Our method addresses the missing modalities problem via knowledge transfer from two generative adversarial networks. The model exhibits superior performance for predicting AD versus healthy control, even with missing modalities. This could have an important impact from the patient point of view since certain clinical tests may not be necessary or available to a given individual.
Variational dynamics as a ground-state problem on a quantum computer

We propose a variational quantum algorithm to study the real time dynamics of quantum systems as a ground-state problem. The method is based on the original proposal of Feynman and Kitaev to encode time into a register of auxiliary qubits. We prepare the Feynman-Kitaev Hamiltonian acting on the composed system as a qubit operator and find an approximate ground state using the Variational Quantum Eigensolver. We apply the algorithm to the study of the dynamics of a transverse field Ising chain with an increasing number of spins and time steps, proving a favorable scaling in terms of the number of two qubit gates. Through numerical experiments, we investigate its robustness against noise, showing that the method can be use to evaluate dynamical properties of quantum systems and detect the presence of dynamical quantum phase transitions by measuring Loschmidt echoes.