Reviewerly/exHarmony huggingface dataset:
INTERACTION EFFECT OF INFORMALITY AND CORRUPTION ON INCOME INEQUALITY IN NIGERIA

Engagement in informal economic activities serves as a survival strategy for underprivileged majority in developing economies with high level of corruption. Consequently, this study examined the main and interaction effects of informality and corruption on income inequality in Nigeria from 1996 to 2020 using autoregressive distributed lag-bound testing technique. The study result showed evidence of long-run relationship among informality, corruption and income inequality. The main effects of informality and corruption on income inequality are negative and statistically significant in both the short and long run. However, corruption reduction in a year was found to reduce income inequality in the subsequent year. Furthermore, the interaction effect of informality and corruption on income inequality was found to be negative and statistically significance in both the short run and the long run. Corruption reduction was found to be a necessary but not sufficient condition for reduction of inequality. Consequently, this study recommended creation of socioeconomic environment conducive for the growth, expansion and eventual formalization of informal businesses. The study also recommended that inequality-reduction be made the end goal of corruption-reduction by ensuring that the proceeds from successful anticorruption campaign are channelled to policies and public projects which redistribute income to the less privileged.
Classification of Healthcare Service Reviews with Sentiment Analysis to Refine User Satisfaction

In natural language processing, sentiment analysis determines the polarity of a message based on lexical emotion. This technique is utilized intensively in service sectors to study the level of consumer satisfaction. However, the healthcare service field lacks such practice to detail responses in existing feedback systems. A proposed application which implements sentiment analysis is developed for improvement. User reviews are classified according to their word influences, namely positive, negative and neutral states. In addition, topic modelling is included to organize them in several service themes. A graphical user interface, GUI which records the analytical results is presented to users for interaction. This approach does not only benefit patients to choose their desired medical centres, but also healthcare management who wish to enhance their service quality.
Improving Contrastive Learning of Sentence Embeddings with Case-Augmented Positives and Retrieved Negatives

Following SimCSE, contrastive learning based methods have achieved the state-of-the-art (SOTA) performance in learning sentence embeddings. However, the unsupervised contrastive learning methods still lag far behind the supervised counterparts. We attribute this to the quality of positive and negative samples, and aim to improve both. Specifically, for positive samples, we propose switch-case augmentation to flip the case of the first letter of randomly selected words in a sentence. This is to counteract the intrinsic bias of pre-trained token embeddings to frequency, word cases and subwords. For negative samples, we sample hard negatives from the whole dataset based on a pre-trained language model. Combining the above two methods with SimCSE, our proposed Contrastive learning with Augmented and Retrieved Data for Sentence embedding (CARDS) method significantly surpasses the current SOTA on STS benchmarks in the unsupervised setting.
Prediction of Incident Dementia Using Patient Temporal Health Status

Dementia is one of the most prevalent health problems in the aging population. Despite the significant number of people affected, dementia diagnoses are often significantly delayed, missing opportunities to maximize life quality. Early identification of older adults at high risk for dementia may help to maximize current quality of life and to improve planning for future health needs in dementia patients. However, most existing risk prediction models predominantly use static variables, not considering temporal patterns of health status. This study used an attention-based time-aware model to predict incident dementia that incorporated longitudinal temporal health conditions. The predictive performance of the time-aware model was compared with three traditional models using static variables and demonstrated higher predictive power.
Technical practices used by information literacy and media information literacy services to enable academic libraries to handle the COVID-19 pandemic

This study analyses the techniques and procedures that were developed and the changes that took place in the National Autonomous University of Mexico (UNAM) and the Autonomous University of Puebla (BUAP), both in Mexico, and the University of Minnesota Duluth (UMD), in the United States of America. To face the crisis of the COVID-19 pandemic, librarians in these institutions improved their Information Literacy (IL) and Media Information Literacy (MIL) programmes. Design / methodology / approach: This study has a mixed methodology with a comparative analysis. For this purpose, data shows the universities’ contexts: the communities of students, teachers, researchers, and librarians, and the e-learning strategies of IL and MIL programmes. Findings: As part of the results of the crowdsourcing collaboration between the UMD, UNAM and BUAP, the study shows the different online learning communities and their innovations. Originality: Although there is theoretical knowledge about IL and MIL in Mexican universities and University of Minnesota Duluth, the e-learning strategies used by their librarians in this document sought to provide technical solutions and other options for a virtual work scheme that responded to the specific problems presented by COVID-19. In this case, the framework for creating online library services was designed by their librarians for their communities in the context of the current crisis, even when online services had already been established for more than ten years. Research limitations / implications: The technological infrastructure, the professionalisation of the library staff and a lack of knowledge of the new virtual teaching-learning needs. Practical implications: Analysis of tools for virtual teaching-learning services, description of strategies used by library staff, results and feedback. Social implications: IL and MIL strategies created in a variety of contexts can be enhanced by library collaboration in a fully virtual setting. Libraries with better technological infrastructure play a decisive role.
Learning Dialogue Representations from Consecutive Utterances

Learning high-quality dialogue representations is essential for solving a variety of dialogueoriented tasks, especially considering that dialogue systems often suffer from data scarcity.In this paper, we introduce Dialogue Sentence Embedding (DSE), a self-supervised contrastive learning method that learns effective dialogue representations suitable for a wide range of dialogue tasks.DSE learns from dialogues by taking consecutive utterances 1 of the same dialogue as positive pairs for contrastive learning.Despite its simplicity, DSE achieves significantly better representation capability than other dialogue representation and universal sentence representation models.We evaluate DSE on five downstream dialogue tasks that examine dialogue representation at different semantic granularities.Experiments in few-shot and zero-shot settings show that DSE outperforms baselines by a large margin.For example, it achieves 13% average performance improvement over the strongest unsupervised baseline in 1-shot intent classification on 6 datasets. 2 We also provide analyses on the benefits and limitations of our model.
Quantum limits to resolution and discrimination of spontaneous emission lifetimes

In this work we investigate the quantum information theoretical limits to several tasks related to lifetime estimation and discrimination of a two-level spontaneous optical emitter. We focus in particular on the model problem of resolving two mutually incoherent exponential decays with highly overlapping temporal probability profiles. Mirroring recent work on quantum-inspired super-resolution of point emitters, we find that direct lifetime measurement suffers from an analogue of "Rayleigh's Curse" when the time constants of the two decay channels approach one another. We propose alternative measurement schemes that circumvent this limit, and also demonstrate superiority to direct measurement for a related binary hypothesis test. Our findings add to a growing list of examples in which a quantum analysis uncovers significant information gains for certain tasks in opto-molecular metrology that do not rely on multiphoton interference, but evidently do benefit from a more thorough exploitation of the coherence properties of single photons.
Refined descriptive sampling simulated annealing algorithm for solving the traveling salesman problem

Abstract The simulated annealing (SA) algorithm is a popular intelligent optimization algorithm which has been successfully applied in many fields. In this paper, we propose a software component under the Windows environment called goRDS which implements a refined descriptive sampling (RDS) number generator of high quality in the MATLAB programming language. The aim of this generator is to sample random inputs through the RDS method to be used in the Simple SA algorithm with swap operator. In this way, the new probabilistic meta-heuristic algorithm called RDS-SA algorithm will enhance the simple SA algorithm with swap operator, the SA algorithm and possibly its variants with solutions of better quality and precision. Towards this goal, the goRDS generator was highly tested by adequate statistical tests and compared statistically to the random number generator (RNG) of MATLAB, and it was proved that goRDS has passed all tests better. Simulation experiments were carried out on the benchmark traveling salesman problem (TSP) and the results show that the solutions obtained with the RDS-SA algorithm are of better quality and precision than those of the simple SA algorithm with swap operator, since the software component goRDS represents the probability behavior of the SA input random variables better than the usual RNG.
An Explainable and Evolving Car Driver Identification System based on Decision Trees

Shared mobility represents a more and more widespread model ensuring several advantages for citizens and reducing gas emissions. The birth of car-sharing models drives the necessity to use car monitoring systems able to reduce the possibility that unauthorized people drive a certain car. In this paper, we discuss the architecture of car driver identification systems based on incremental fuzzy decision trees. The main features of the proposed system are i) the explainability, namely the possibility of giving explanations regarding its decisions, provided in terms of linguistic rules, and ii) the possibility of continuously updating the classification model. We show the preliminary results of an experimental campaign in which we compare both fuzzy and non-fuzzy incremental decision trees, both in terms of classification performance and model complexity/explainability.
Graph Data Science Using Neo4j

This chapter focuses on the application of graph data science in business using Neo4j graph technology to illustrate examples because of its commercial popularity. It explores the steps of graph data science adoption, starting with knowledge graphs and graph analytics, moving to graph feature engineering and graph embedding, and ending with graph networks for graph native learning. Graph Statistics provides basic measures about our graph, such as the number of nodes and distribution of relationships. Graph analytics builds upon graph statistics by answering specific questions and gaining insights from connections in existing or historical data. One machine learning scenario would be to extract graph features for use in a binary classifier to predict mules. The world is driven by connections and revealing the meaning behind these relationships drives breakthrough across industries.
Quantum annealing for hard 2-satisfiability problems: Distribution and scaling of minimum energy gap and success probability

In recent years, quantum annealing has gained the status of being a promising candidate for solving various optimization problems. Using a set of hard 2-satisfiabilty (2-SAT) problems, consisting of upto 18-variables problems, we analyze the scaling complexity of the quantum annealing algorithm and study the distributions of the minimum energy gap and the success probability. We extend the analysis of the standard quantum annealing Hamiltonian by introducing an additional term, the trigger Hamiltonian, which can be of two types : ferromagnetic and antiferromagnetic. We use these trigger Hamiltonians to study their influence on the success probability for solving the selected 2-SAT problems. We found that although the scaling of the run-time is exponential for the standard and modified quantum annealing Hamiltonians, the scaling constant in case of adding the trigger Hamiltonians can be significantly smaller. Furthermore, certain choices for the trigger Hamiltonian and annealing times can result in a better scaling than that for simulated annealing. Lastly, we also use the quantum annealers of D-Wave Systems Inc. to study their performance in solving the 2-SAT problems and compare it with the simulation results.
HDPView

How can we explore the unknown properties of high-dimensional sensitive relational data while preserving privacy? We study how to construct an explorable privacy-preserving materialized view under differential privacy. No existing state-of-the-art methods simultaneously satisfy the following essential properties in data exploration: workload independence, analytical reliability (i.e., providing error bound for each search query), applicability to high-dimensional data, and space efficiency. To solve the above issues, we propose HDPView, which creates a differentially private materialized view by well-designed recursive bisected partitioning on an original data cube, i.e., count tensor. Our method searches for block partitioning to minimize the error for the counting query, in addition to randomizing the convergence, by choosing the effective cutting points in a differentially private way, resulting in a less noisy and compact view. Furthermore, we ensure formal privacy guarantee and analytical reliability by providing the error bound for arbitrary counting queries on the materialized views. HDPView has the following desirable properties: (a) Workload independence, (b) Analytical reliability, (c) Noise resistance on high-dimensional data, (d) Space efficiency. To demonstrate the above properties and the suitability for data exploration, we conduct extensive experiments with eight types of range counting queries on eight real datasets. HDPView outperforms the state-of-the-art methods in these evaluations.
Text Classification Model Explainability for Keyword Extraction – Towards Keyword-Based Summarization of Nursing Care Episodes

Tools to automate the summarization of nursing entries in electronic health records (EHR) have the potential to support healthcare professionals to obtain a rapid overview of a patient's situation when time is limited. This study explores a keyword-based text summarization method for the nursing text that is based on machine learning model explainability for text classification models. This study aims to extract keywords and phrases that provide an intuitive overview of the content in multiple nursing entries in EHRs written during individual patients' care episodes. The proposed keyword extraction method is used to generate keyword summaries from 40 patients' care episodes and its performance is compared to a baseline method based on word embeddings combined with the PageRank method. The two methods were assessed with manual evaluation by three domain experts. The results indicate that it is possible to generate representative keyword summaries from nursing entries in EHRs and our method outperformed the baseline method.
To use or not to use the SIMD gather instruction?

The Single Instruction Multiple Data (SIMD) paradigm became a core principle for optimizing query operators. Until now, only the LOAD/STORE instructions are considered to be efficient enough to achieve the expected speedups, while avoiding GATHER/SCATTER is considered almost imperative. However, the GATHER instruction offers a very flexible way to populate SIMD registers with data elements coming from non-consecutive memory locations. As we will show within the paper, the GATHER instruction can achieve the same performance as the LOAD instruction, if applied properly. To enable the proper usage, we outline a novel access pattern which then allows fine-grained, partition-based SIMD implementations using the GATHER instruction.
Knowledge Transfer-Based Sparse Deep Belief Network

Deep learning has made remarkable achievements in various applications in recent years. With the increasing computing power and the ``black box'' problem of neural networks, however, the development of deep neural networks (DNNs) has entered a bottleneck period. This article proposes a novel deep belief network (DBN) based on knowledge transfer and optimization of the network structure. First, a neural-symbolic model is proposed to extract rules to describe the dynamic operation mechanism of the deep network. Second, knowledge fusion is proposed based on the merge and deletion of the extracted rules from the DBN model. Finally, a new DNN, knowledge transfer-based sparse DBN (KT-SDBN) is constructed to generate a sparse network without excessive information loss. In comparison with DBN, KT-SDBN has a more sparse network structure and better learning performance on the existing knowledge and data. The experimental results in the benchmark data indicate that KT-SDBN not only has effective feature learning performance with 30&#x0025; of the original network parameters but also shows a large compression rate that is far larger than other structure optimization algorithms.
Enhancing Superradiance in Spectrally Inhomogeneous Cavity QED Systems with Dynamic Modulation

The Tavis–Cummings system, wherein emitters collectively interact with the same optical mode, has been a long-standing model to study collective photon emission phenomena such as superradiance. However, disorder in the resonant frequencies of the quantum emitters can perturb these effects. In this paper, we study the impact of dynamic modulation on the interplay between superradiance and spectral disorder. Through numerical simulations and analytical calculations, we provide evidence that the effective cooperativity of the superradiant mode can be multiplicatively enhanced with a quantum control protocol modulating the resonant frequency of the optical mode. Our results are relevant to experimental demonstration of superradiance in solid-state quantum optical systems, wherein the spectral disorder is a signficant technological impediment toward achieving photon-mediated emitter–emitter couplings.
Memetic Algorithms for Spatial Partitioning Problems

Spatial optimization problems (SOPs) are characterized by spatial relationships governing the decision variables, objectives, and/or constraint functions. In this article, we focus on a specific type of SOP called spatial partitioning, which is a combinatorial problem due to the presence of discrete spatial units. Exact optimization methods do not scale with the size of the problem, especially within practicable time limits. This motivated us to develop population-based metaheuristics for solving such SOPs. However, the search operators employed by these population-based methods are mostly designed for real-parameter continuous optimization problems. For adapting these methods to SOPs, we apply domain knowledge in designing spatially-aware search operators for efficiently searching through the discrete search space while preserving the spatial constraints. To this end, we put forward a simple yet effective algorithm called swarm-based spatial memetic algorithm (SPATIAL) and test it on the school (re)districting problem. Detailed experimental investigations are performed on real-world datasets to evaluate the performance of SPATIAL. Besides, ablation studies are performed to understand the role of the individual components of SPATIAL. Additionally, we discuss how SPATIAL~is helpful in the real-life planning process and its applicability to different scenarios and motivate future research directions.
Transmission Line Object Detection Method Based on Label Adaptive Allocation

Inspection of the integrality of components and connecting parts is an important task to maintain safe and stable operation of transmission lines. In view of the fact that the scale difference of the auxiliary component in a connecting part is large and the background environment of the object is complex, a one-stage object detection method based on the enhanced real feature information and the label adaptive allocation is proposed in this study. Based on the anchor-free detection algorithm FCOS, this method is optimized by expanding the real feature information of the adjacent feature layer fusion and the semantic information of the deep feature layer, as well as adaptively assigning the label through the idea of pixel-by-pixel detection. In addition, the grading ring image is sliced in original data to improve the proportion of bolts in the dataset, which can clear the appearance features of small objects and reduce the difficulty of detection. Experimental results show that this method can eliminate the background interference in the GT (ground truth) as much as possible in object detection process, and improve the detection accuracy for objects with a narrow shape and small size. The evaluation index AP (average precision) increased by 4.1%. Further improvement of detection accuracy lays a foundation for the realization of efficient real-time patrol inspection.
Avoid Overfitting User Specific Information in Federated Keyword Spotting

Keyword spotting (KWS) aims to discriminate a specific wake-up word from other signals precisely and efficiently for different users. Recent works utilize various deep networks to train KWS models with all users' speech data centralized without considering data privacy. Federated KWS (FedKWS) could serve as a solution without directly sharing users' data. However, the small amount of data, different user habits, and various accents could lead to fatal problems, e.g., overfitting or weight divergence. Hence, we propose several strategies to encourage the model not to overfit user-specific information in FedKWS. Specifically, we first propose an adversarial learning strategy, which updates the downloaded global model against an overfitted local model and explicitly encourages the global model to capture user-invariant information. Furthermore, we propose an adaptive local training strategy, letting clients with more training data and more uniform class distributions undertake more local update steps. Equivalently, this strategy could weaken the negative impacts of those users whose data is less qualified. Our proposed FedKWS-UI could explicitly and implicitly learn user-invariant information in FedKWS. Abundant experimental results on federated Google Speech Commands verify the effectiveness of FedKWS-UI.