Reviewerly/exHarmony huggingface dataset:
Talent recommendation based on attentive deep neural network and implicit relationships of resumes

Talent recruitment has become a crucial issue for companies since finding suitable candidates from the massive data on potential candidates from online talent platforms is a challenging task. However, extant studies mainly focus on the scalability and inference ability of models, while the dynamic variability of the importance of each feature in different scenarios is barely addressed. Besides, there is a lack of research on how to depict the hidden potential preference of the job which cannot be derived from job requirements. In this paper, we propose a two-stage resume recommendation model based on deep learning and attention mechanisms, especially considering the latent preference information hidden in the hired employee resumes, named the Attentive Implicit Relationship-Aware Neural Network (AIRANN) model. Specifically, a novel mechanism is proposed herein to extract the hidden potential preference of the corresponding job by deriving the implicit relationship between the target resume and hired employees’ resumes. Existing studies have not considered such resume implicit relationships. Moreover, we propose a Feature Co-Attention mechanism to capture the dynamic interactive importance within the non-text features of both resumes and jobs. For different jobs, the suitability of resumes would be valued from different aspects, including resume implicit relationships, as well as textual and non-textual features. Accordingly, an Aspect-attention mechanism is designed herein to automatically adjust the variant importance of each aspect. Finally, extensive experiments are conducted on a real-world company dataset. The experiment results of ablation studies demonstrate the effectiveness of each mechanism in the proposed AIRANN model. The experiment results also show that the proposed AIRANN model outperforms other baseline methods, showing a general improvement of 13.31%, 12.49%, 6.5% and 7.17% over the state-of-the-art baseline under F1@6, F1@15, NDCG@6 and NDCG@15, respectively.
Research progresses and problems of sedimentary-metasedimentary rock-hosted cobalt deposits

沉积岩-变质型钴矿床占全球超过50%的钴资源储量,产量约占全球的65%以上,这类钴矿床主要分布在中非赞比亚-刚果(金)、美国爱达荷,及我国辽吉、山西、青海等地的沉积砂页岩、海底热水沉积岩及其变质岩系中。目前国际上对沉积岩-变沉积岩型钴矿的区域成矿地质背景、含矿建造、钴的赋存状态、后期变质与构造作用、成矿时代等方面有较多的研究,但在沉积-变质-热液改造等不同地质过程中钴的地球化学行为、沉积成矿条件与古海洋环境、钴的富集机理及其控制因素等方面的认识仍存在分歧和有待深入研究的问题。本文在综述国内外典型铜钴成矿带地质背景、含矿建造和矿床地质特征等的基础上,总结了各成矿带的主要成矿条件与成矿规律,分析了各矿带成矿流体和成矿物质的来源、富矿控制要素和成矿机制;提出控制钴沉积成矿的制约因素主要是大陆裂解、海洋氧化还原条件、沉积相与沉积环境等,而要形成富矿的关键是后期变质作用或热液活动的改造叠加。同时作者对全球沉积-变沉积型钴矿的时空分布规律及控制因素进行了探讨,提出今后应在古气候与古海洋环境、物质来源与矿源层、后期热液改造作用与钴元素超常富集成矿机理等方面开展详细和深入的研究。
Money in the time of crypto

This paper investigates the evolution of cryptocurrencies. By nature and essence, Bitcoin challenged and implicitly threatened central bank money and its role in the monetary system. Meanwhile, central banks have been studying cryptocurrencies and launched pilot projects on their own digital currency, the Central Bank Digital Currency. Until recently, most economists considered Bitcoin merely as a speculative asset; however, the El Salvador decision in 2021 to establish it as a legal tender (through the Bitcoin Law) questions the status quo perception of Bitcoin. Given El Salvador’s legal obligation by law of their acceptance, allowing tax payments to the government and debts to be settled using Bitcoin, the Bitcoin Law challenges the boundaries of money. In light of the El Salvador experience, we consider different perspectives on the nature of money, allowing us to reject or include Bitcoin inside the money spectrum.
Strategies for a practical advantage of fault-tolerant circuit design in noisy trapped-ion quantum computers

Fault-tolerant quantum error correction provides a strategy to protect information processed by a quantum computer against noise which would otherwise corrupt the data. A fault-tolerant universal quantum computer must implement a universal gate set on the logical level in order to perform arbitrary calculations to in principle unlimited precision. In this paper we characterize the recent demonstration of a fault-tolerant universal gate set in a trapped-ion quantum computer [Postler et al., Nature (London) 605, 7911 (2022)] and identify aspects to improve the design of experimental setups to reach an advantage of logical over physical qubit operation. We show that various criteria to assess the break-even point for fault-tolerant quantum operations are within reach for the ion trap quantum computing architecture under consideration. Furthermore, we analyze the influence of crosstalk in entangling gates for logical state preparation circuits. These circuits can be designed to respect fault tolerance for specific microscopic noise models. We find that an experimentally informed depolarizing noise model captures the essential noise dynamics of the fault-tolerant experiment that we consider, and crosstalk is negligible in the currently accessible regime of physical error rates. For deterministic Pauli state preparation, we provide a fault-tolerant unitary logical qubit initialization circuit, which can be realized without in-sequence measurement and feed-forward of classical information. Additionally, we show that nondeterministic state preparation schemes, i.e., repeat until success, for logical Pauli and magic states perform with higher logical fidelity over their deterministic counterparts for the current and anticipated future regime of physical error rates. Our results offer guidance on improvements of physical qubit operations and validate the experimentally informed noise model as a tool to predict logical failure rates in quantum computing architectures based on trapped ions.
Autoencoder for Image Retrieval System using Deep Learning Technique with Tensorflow and Kears

In today's era image retrieval is most emerging trends of digital image processing techniques. Traditional methods for retrieval such as Text and Query based image retrieval of image it will take more time for searching and retrieving images from large datasets. So, to reduce the time for searching and results, in this research work we used deep learning neural network technique is called Autoencoder. Autoencoder is neutral networks which reconstruct images. The proposed work, the image are retrieves similar visual content textures, color and shape of image this technique recognized as “Content Based Image Retrieval (CBIR)”. Current research work, we used the autoencoder technique to form image into feature encoded vector format which will helpful for searching image from larger dataset. Proposed the encoder architecture to evaluate the input image then applied two training approaches pre-train and post train to generate good quality encoded representation. Three autoencoder are used for this research work Simple autoencoder, Deep autoencoder and Convolutional autoencoder. To reduced search indexing time, for extracting similar images from the dataset we used Approximate Nearest Neighbor (ANN) approach. Finally, developed end to end application system for image retrieval on MNIST and CIFER 10 datasets.
A metaverse-oriented CP-ABE scheme with cryptographic reverse firewall

Metaverse is an immersive, hyperspace virtual reality space. Due to its characteristics of hyperspace and immersive realism, metaverse has a bright future, but ubiquitous user analysis and calculation make it difficult to achieve one-to-multiple data sharing. As an innovative encryption algorithm, ciphertext-policy attribute-based encryption (CP-ABE) can realize one-to-multiple secure file sharing through fine-grained access control. Many evidences show that attackers may add backdoors to internal devices to make encryption algorithms insecure, CP-ABE also faces the above threats. In order to deal with such attacks, researchers put forward the notion of cryptographic reverse firewall (CRF), which has the ability of resistance to insider attack. However, the existing ABE scheme supporting CRF have the problems of low efficiency and no support for malicious user tracking. To improve safety and efficiency, we constructed an efficient CP-ABE scheme supporting CRF protection which has function of outsourcing decryption, offline encryption and black-box tracking.
Transformer for multiple object tracking: Exploring locality to vision

Multi-object tracking (MOT) is a critical task in various domains, such as traffic analysis, surveillance, and autonomous vehicles. The joint-detection-and-tracking paradigm has been extensively researched, which is faster and more convenient for training and deploying over the classic tracking-by-detection paradigm while achieving state-of-the-art performance. This paper explores the possibilities of enhancing the MOT system by leveraging the prevailing convolutional neural network (CNN) and a novel vision transformer technique Locality. There are several deficiencies in the transformer adopted for computer vision tasks. While the transformers are good at modeling global information for a long embedding, the locality mechanism, which learns the local features, is missing. This could lead to negligence of small objects, which may cause security issues. We combine the TransTrack MOT system with the locality mechanism inspired by LocalViT and find that the locality-enhanced system outperforms the baseline TransTrack by 5.3% MOTA on the MOT17 dataset.
An overview of internet of things (IoT): Definitions, architecture, security, applications and future directions

The use of technology has been constantly increasing in our lives and it is affecting it greatly.There has been no doubt in this claim that technology is headed in the direction of automation and is making steady progress.If we look at the definition of technology, it can be described as an act of progress that makes it easier to complete a task.Isn't it the very basic principle of technology to make our lives easier by leaving fewer things to be explicitly done by us?It might be making us all lazier every day, or one might argue that it is giving us far greater time to pursue whatever we desire.Whatever might be the effect, there is no doubt that automation is the future and the place that it is happening the most significant is right in our homes.Internet of things (IoT) makes our lives easier by automating every task at our homes starting from turning lights ON or OFF to opening or closing doors.The use of IoT is not restricted to homes but is also used in autonomous vehicles are commonly known as self-driving cars and many other applications.In this paper; Firstly, various definitions of IoT are introduced; Secondly, the architecture of IoT is discussed; Thirdly, the major challenges in the security of IoT which need addressing by the research community and corresponding potential solutions are investigated; Fourthly, some open issues related to the IoT applications are explored; Finally, discussion of IoT and future directions are systematically reviewed.
Exploring blockchain for disaster prevention and relief: A comprehensive framework based on industry case studies

Abstract Blockchain technology has been suggested as a groundbreaking solution for disaster prevention and relief. However, there is a dearth of both practical applications and systematic academic research into how those existing blockchain solutions actually help to prevent disasters and/or alleviate their negative impacts. The purpose of this paper was to extend previous research by investigating existing blockchain solutions using a rigorous multiple case study approach. Based on stakeholder theory, a structured framework is developed that examines the roles of key stakeholders in disaster situations, illustrates organizations' motivations to deploy blockchain technology, identifies various use cases, lists the relevant blockchain properties, and highlights contingency factors for successful implementations. The findings demonstrate that blockchain applications can improve both disaster prevention as well as disaster relief efforts by streamlining information flows and augmenting the capabilities of core stakeholders. Furthermore, numerous propositions can be derived from the framework that can be used as starting points for further in‐depth investigations. This study familiarizes practitioners with blockchain's potential for disaster management and provides academics with a theory‐based foundation for future research.
The threat of robots to career sustainability, and the pivotal role of knowledge management and human capital

With the remarkable progress made in the field of artificial intelligence, there is growing concern about the possibility of robots posing a threat or danger to humans and the robot is crowding more and more people out of their jobs. The epidemic period also witnessed an unparalleled increase in the demand for technology products. This empirical study investigates several aspects of Knowledge Management (KM), Human Capital (HC) and Career Sustainability (CS): first, the relationship between KM dimensions, namely Technical, Cultural, Human and Structural Knowledge Management (TKM, CKM, HKM, SKM) on HC and CS in Malaysian public universities; and, secondly, the moderating role of HC on the relationship between these KM dimensions and CS. A research model was developed to test these hypothesized relationships. The model was tested on data from 251 lecturers, employing structural equation modelling PLS-SEM. The results show that TKM, CKM and HKM, but not SKM, have a significant impact on CS. HC moderates the relationship between TKM, SKM and HKM and CS but not CKM. The results provide useful insights for researchers and academics, outlining the practical and theoretical implications and future research.
Tunable directional photon scattering from a pair of superconducting qubits

The ability to control the direction of scattered light is crucial to provide flexibility and scalability for a wide range of on-chip applications, such as integrated photonics, quantum information processing, and nonlinear optics. Tunable directionality can be achieved by applying external magnetic fields that modify optical selection rules, by using nonlinear effects, or interactions with vibrations. However, these approaches are less suitable to control microwave photon propagation inside integrated superconducting quantum devices. Here, we demonstrate on-demand tunable directional scattering based on two periodically modulated transmon qubits coupled to a transmission line at a fixed distance. By changing the relative phase between the modulation tones, we realize unidirectional forward or backward photon scattering. Such an in-situ switchable mirror represents a versatile tool for intra- and inter-chip microwave photonic processors. In the future, a lattice of qubits can be used to realize topological circuits that exhibit strong nonreciprocity or chirality.
A divide and conquer approach to maximise deep learning mammography classification accuracies

Breast cancer claims 11,400 lives on average every year in the UK, making it one of the deadliest diseases. Mammography is the gold standard for detecting early signs of breast cancer, which can help cure the disease during its early stages. However, incorrect mammography diagnoses are common and may harm patients through unnecessary treatments and operations (or a lack of treatment). Therefore, systems that can learn to detect breast cancer on their own could help reduce the number of incorrect interpretations and missed cases. Various deep learning techniques, which can be used to implement a system that learns how to detect instances of breast cancer in mammograms, are explored throughout this paper. Convolution Neural Networks (CNNs) are used as part of a pipeline based on deep learning techniques. A divide and conquer approach is followed to analyse the effects on performance and efficiency when utilising diverse deep learning techniques such as varying network architectures (VGG19, ResNet50, InceptionV3, DenseNet121, MobileNetV2), class weights, input sizes, image ratios, pre-processing techniques, transfer learning, dropout rates, and types of mammogram projections. This approach serves as a starting point for model development of mammography classification tasks. Practitioners can benefit from this work by using the divide and conquer results to select the most suitable deep learning techniques for their case out-of-the-box, thus reducing the need for extensive exploratory experimentation. Multiple techniques are found to provide accuracy gains relative to a general baseline (VGG19 model using uncropped 512 × 512 pixels input images with a dropout rate of 0.2 and a learning rate of 1 × 10 −3 ) on the Curated Breast Imaging Subset of DDSM (CBIS-DDSM) dataset. These techniques involve transfer learning pre-trained ImagetNet weights to a MobileNetV2 architecture, with pre-trained weights from a binarised version of the mini Mammography Image Analysis Society (mini-MIAS) dataset applied to the fully connected layers of the model, coupled with using weights to alleviate class imbalance, and splitting CBIS-DDSM samples between images of masses and calcifications. Using these techniques, a 5.6% gain in accuracy over the baseline model was accomplished. Other deep learning techniques from the divide and conquer approach, such as larger image sizes, do not yield increased accuracies without the use of image pre-processing techniques such as Gaussian filtering, histogram equalisation and input cropping.
Leopard seal optimization (LSO): A natural inspired meta-heuristic algorithm

The main objective of this paper is to introduce a new NIO algorithm inspired from the hunting strategy of the leopard seals called Leopard Seal Optimization (LSO)to provide a simple swarm intelligence algorithm that have a high flexibility to solve real-time engineering problems in a fast and more accurate manner without falling into local optima problem. LSO is compared against recent NIO algorithms considering feature selection for disease diagnosing as the underlying optimization problem. In experimental results, LSO has been statistically tested against a recent six swarm intelligence algorithms using Wilcoxon test and t-test with significance level equals 0.05 based on the common five benchmark functions. These recent algorithms are called Tuna Swarm Optimization (TSO), Pelican Optimization Algorithm (POA), Cat and Mouse-Based Optimization (CMBO), Aphid–Ant Mutualism (AAM), White Shark Optimizer (WSO), and Red Piranha Optimization (RPO). The statistical analysis proved that LSO outperforms other recent techniques in most cases where the most tested results are less than 0.05. Then, LSO has been tested against the same algorithms in binary version as feature selection algorithms using two metrics called accuracy and execution time. It is noted that LSO is faster and more accurate than other algorithms to determine the optimal set of features at all numbers of search agents. Finally, it is concluded that LSO outperforms other techniques using accuracy, execution time, Wilcoxon test, and t-test metrics. Compared to the recent techniques using the maximum iteration number=400, LSO provided the maximum accuracy value that equals 97.62% at the search agents number=120 and the minimum execution time that equals 1314 at the search agents number= 40.
Improved Blending Attention Mechanism in Visual Question Answering

Visual question answering (VQA) has attracted more and more attention in computer vision and natural language processing. Scholars are committed to studying how to better integrate image features and text features to achieve better results in VQA tasks. Analysis of all features may cause information redundancy and heavy computational burden. Attention mechanism is a wise way to solve this problem. However, using single attention mechanism may cause incomplete concern of features. This paper improves the attention mechanism method and proposes a hybrid attention mechanism that combines the spatial attention mechanism method and the channel attention mechanism method. In the case that the attention mechanism will cause the loss of the original features, a small portion of image features were added as compensation. For the attention mechanism of text features, a self-attention mechanism was introduced, and the internal structural features of sentences were strengthened to improve the overall model. The results show that attention mechanism and feature compensation add 6.1% accuracy to multimodal low-rank bilinear pooling network.
AnnoBERT: Effectively Representing Multiple Annotators’ Label Choices to Improve Hate Speech Detection

Supervised machine learning approaches often rely on a "ground truth" label. However, obtaining one label through majority voting ignores the important subjectivity information in tasks such hate speech detection. Existing neural network models principally regard labels as categorical variables, while ignoring the semantic information in diverse label texts. In this paper, we propose AnnoBERT, a first-of-its-kind architecture integrating annotator characteristics and label text with a transformer-based model to detect hate speech, with unique representations based on each annotator's characteristics via Collaborative Topic Regression (CTR) and integrate label text to enrich textual representations. During training, the model associates annotators with their label choices given a piece of text; during evaluation, when label information is not available, the model predicts the aggregated label given by the participating annotators by utilising the learnt association. The proposed approach displayed an advantage in detecting hate speech, especially in the minority class and edge cases with annotator disagreement. Improvement in the overall performance is the largest when the dataset is more label-imbalanced, suggesting its practical value in identifying real-world hate speech, as the volume of hate speech in-the-wild is extremely small on social media, when compared with normal (non-hate) speech. Through ablation studies, we show the relative contributions of annotator embeddings and label text to the model performance, and tested a range of alternative annotator embeddings and label text combinations.
Probabilistic Matrix Factorization Recommendation Approach for Integrating Multiple Information Sources

Most previous studies on matrix factorization (MF)-based collaborative filtering (CF) have focused solely on user rating information for predicting recommendations. However, to further enhance the performance of recommender systems (RSs), it is important to also consider review information and rating reliability in the model. This article proposes a new probabilistic MF (PMF)-based CF method that integrates multiple information sources to provide reliable predictions. First, we introduce a sentiment-based PMF to handle user reviews and fit the normalized sentiment information obtained from our previously proposed sentiment analysis method. We also consider the helpfulness of reviews to highlight their reliability and effectiveness in this model. Subsequently, our proposed noise detection method is adopted to determine the reliability of user ratings, and then the rating matrix is transformed into a binary reliability matrix. A rating reliability-based PMF through Bernoulli distribution is then proposed to factorize it. To effectively integrate three types of information (ratings, reviews, and rating reliability) into a PMF procedure, we design a weight matrix using the proposed weighting strategy to generate a set of comprehensive prediction ratings with corresponding reliability probabilities. Experiments on four Amazon datasets demonstrate that our model outperforms comparison methods in terms of comprehensive evaluation.